Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , 8 10 13 and Chemistry 4 and Physics Discussions , Atmospheric 6 , K. Corbin , S. R. Kawa 9 , R. G. Prinn variability 6,1 11 4 , D. Belikov , A. Ito 5 12 , P. I. Palmer 12 , M. P. Chipperfield , P. Hess 7 9 , P. Bousquet 18768 18767 , L. Meng 4,3,2 9 6 , E. Gloor 11 , M. Krol 2,3 , P. Cameron-Smith 8 , A. Fraser , S. Maksyutov , and C. Wilson 5 1 10 , H. Bian 7 , Z. Loh , S. Houweling , R. Saito 1 10 13 Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric School of Geosciences, University of Edinburgh, King’s Buildings, West Mains Road, Cornell University, 2140 Snee Hall, Ithaca,Center NY for 14850, Global USA Change Science, Building 54, Massachusetts Institute of Technology, Wageningen University and Research Centre, Droevendaalsesteeg 4, Universite de Versailles Saint Quentin enCenter Yvelines for (UVSQ), Global GIF Environmental sur Research, YVETTE, National France Institute for Environmental Studies, Atmospheric, Earth, and Energy Division, Lawrence Livermore National Laboratory, Goddard Earth Sciences and Technology Center, NASA Goddard Space Flight Center, Institute for Climate and Atmospheric Science, School of Earth and Environment, and Physics (ACP). Please refer to the corresponding final paper in ACP if available. This discussion paper is/has been under review for the journal Atmospheric Chemistry Research Institute for Global Change/JAMSTEC, 3173-25 Show-machi, SRON Netherlands Institute for Space Research, Sorbonnelaan 2, Institute for Marine and Atmospheric Research Utrecht (IMAU), Princetonplein 5, 4 6708 PB Wageningen, The5 Netherlands 6 16-2 Onogawa, Tsukuba, Ibaraki,7 305-8506, Japan 7000 East Avenue, Livermore,8 CA 94550, USA Code 613.3, Greenbelt, MD9 20771, USA University of Leeds, Leeds,10 LS2 9JT, UK Research, 107–121 Station St.,11 Aspendale, VIC 3195, Australia Edinburgh, EH9 3JN, UK 12 13 Cambridge, MA, 02139, USA Received: 21 June 2011 – Accepted: 21Correspondence June to: 2011 P. – K. Published: Patra ([email protected]) 1 July 2011 Published by Copernicus Publications on behalf of the European Geosciences Union. related species: linking transport, surface flux and chemical loss with CH TransCom model simulations of CH Atmos. Chem. Phys. Discuss., 11,www.atmos-chem-phys-discuss.net/11/18767/2011/ 18767–18821, 2011 doi:10.5194/acpd-11-18767-2011 © Author(s) 2011. CC Attribution 3.0 License. in the troposphere and lower stratosphere P. K. Patra D. Bergmann A. Fortems-Cheiney 1 Yokohama, 2360001, Japan 2 3584 CA Utrecht,3 The Netherlands 3584 CC Utrecht, The Netherlands R. M. Law M. Rigby Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 0.13 yr, simula- D chem- -Fletcher ± 4 , depend- 1 ff 1 for the 1990s 1 − yr 4 emission distributions ) has been designed to 4 0.08 and 4.61 4 ) was simulated to check ± 6 budget is shown to depend 14 Tg CH 4 ± erent emission scenarios were ff 9 and 514 and related species are being conducted 4 ± Rn) to check the subgrid scale transport, and trends and variabilities within the uncertainty . The vertical gradients of all tracers between 18770 18769 4 tracers with di 4 is faster by about 11 % in the 2000s compared 222 4 6 depends on the spatio-temporal variations of the 4 emissions range between 500 to 600 Tg yr and CH observations from SCanning Imaging Absorption Spec- 4 3 4 in the upper troposphere and stratosphere. Most models ) to check the chemical removal by the tropospheric hy- measurements from 8 selected background sites, and to 3 4 CCl 3 3 CCl 3 CCl 3 , CH 6 erences between models. We find that the interhemispheric exchange ff atmospheric lifetimes are estimated to be 9.99 and CH 3 0.18 yr) derived from SF . 6 σ ± 1 CCl 3 , SF ± 4 models. Individual flux components varyand Fung, by 1987; even greater Yan et percentages al., (e.g., 2009). Matthews produce the observed atmosphericof CH the processes involved (Heinet et al., al., 2004; 1997; Chen HouwelingHowever, and further et Prinn, improvements al., 2006; (reduction 1999; Bousquet in Mikalo verse et the al., modeling posterior 2006; emission results Bergamaschi uncertainty) dependemissions et of al., and on in- 2009). sinks, a and betterinverse on estimates quantification of error of global reductions CH (theing in on errors forward the in) model transport the transport. properties prior and Presently, the chemical loss parameterization in the forward able, albeit at a loweret al., precision 2011). (Frankenberg et Significant developments al., inhave 2008; understanding been the Xiong achieved CH et in the al.,1991; past 2008; Gupta few Yoshida et years through al.,2004; forward 1996; modeling Patra Houweling et (e.g., et Fung al., al., et 2009a). al., 2000; Inverse Dentener model et results al., show the 2003; ability Wang of et the al., models to re- The variability of atmosphericsurface CH fluxes, atmospheric transportistry. and In destruction recent due years, measurements toat of OH, an CH increasingly Cl large andinstrumental number O of precision sites (Rasmussen at andet hourly Khalil, or al., 1984; daily Aoki 1998; timetional et intervals programs). Cunnold and al., Satellite with et CH 1990; high al., Dlugokencky troMeter for 2002; Atmospheric CartograpHY WDCGG, (SCIAMACHY),(AIRS), Atmospheric and 2010 Greenhouse Infrared Gases for Sounder Observing a (GOSAT) SATellite are also complete becoming avail- list of observa- by the 1 Introduction respectively, with little interannual variability due to transport and temperature as noted and CH cal transport di rate (1.39 to the 1990s.tions can Up be to explainedbiomass 60 by % burning accounting of for and the the wetlands.likely interannual interannual reached We variations variations also in equilibrium emissions show inemission in from that the growth the the since forward early decadal the CH strongly late average 2000s on growth 1990s. due the rate The troposphere-stratosphere to modeled exchangecal the rate CH grid and flattening structure thus and of to circulation anthropogenic the in model’s the verti- lower stratosphere. The 15-model median CH satellite observations of CH adequately capture the verticaltrends, gradients seasonal in cycles, the interannualsurface stratosphere, variations sites and for the SF interhemispheric averagethe long-term gradients surface at and the the upper troposphere are consistent within the models, revealing verti- els and four modelThe variants transport and and results removalsimulated, of were with six archived net global CH for emissions theand of 2000s, 513 period respectively. Additionally, of sulfur 1990–2007. hexafluoridethe (SF interhemispheric transport, radon ( methyl chloroform (CH droxyl radical (OH). The resultsof are CH compared to monthly or annual mean time series A transport model intercomparison experimentinvestigate (TransCom-CH the roles ofthe surface global methane emissions, distribution. transport Model and simulations chemical were conducted loss using in twelve mod- simulating Abstract 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , . 3 4 3 6 4 up 4 CCl erent . The CCl 3 model ff 4 3 lifetime ) on per 4 protocol on CH 2 4 4 3 , CH 6 ) transport or CCl and its impact 6 3 lifetime ranged 4 4 erences. Detailed ff are distinctly di 4 database and conclusions are ) (Law et al., 1996, 2008; Den- 2 4 (Dlugokencky et al., 1998; Simpson 1 emissions scenarios), chemical loss, − 4 , relative to carbon dioxide (CO erences in model properties. An attempt 4 ff simulation allows us to track the interannual concentration variations in the troposphere. 6 4 5 ppb yr 18772 18771 − ective emission mitigation policies. For example, to ff erent view on transport model di 1 ff − experiment described in this paper is to quantify the Rn) transport on chemically active species (CH 4 222 lifetimes on the model grid structure and transport, as well ) and carbon dioxide (CO 3 6 Rn), which has a radioactive decay half-life of 3.8 days (e.g., budget on model vertical transport in the stratosphere will be 4 CCl 222 3 intercomparison requires the introduction of atmospheric chem- ect of the OH abundance, as constrained by CH ff 4 ) chemistry have also been tested using multiple CTMs, where CH 3 and CH 4 , which may provide a di at 8 surface sites and the salient di Rn). A CH 2 4 222 in an interactive manner, because of its long lifetime. The CH , ), and (2) the e 2 4 4 In Sect. 2, we describe the experimental protocol, followed by the key information on The aim of the TransCom-CH The performance of atmospheric transport models has been investigated within the intercomparison set-up is shown in Fig. 1. CO istry. Additionally, the sourcesfrom and CO atmospheric lifetime ofdocumentation CH of the requested(Patra simulation et is al., available 2010). in Transport theuary model TransCom-CH simulations 1990 are to requested 31 foror December the period 2007, atmospheric of after general 1 a Jan- both circulation spin-up (referred model of here as 2-yr (AGCM) AGCM-nudged). (1988–1989) meteorology A using schematic or analyzed diagram a of TransCom-CH combination of given in Sects. 4 and 5, respectively. 2 Models, measurements and methods Previous TransCom experiments focused on chemically non-reactive species (SF the participating models andon analysis the methodology. comparison of Weand focus model CH this results analysis withis (Sect. atmospheric also 3) observations made of to SF understandshort-lived radioactive possible tracer implications ( ofCH (1) inert tracer (SF Scope for further analysis using the TransCom-CH on these removal rates.spanned only Since a few the years, the previousvariability 18 (IAV) TransCom yr in intercomparison of the SF experiments IHdence of exchange CH rate for theas first the transport time. and We temperature also as discuss drivers the of IAVs depen- in lifetimes. fluxes in the troposphere and stratosphere and of the influence of transport processes The proposed 18-yr simulation period allows a proper quantification of the removal spheric (IH) gradient, seasonal cycle,dependence synoptic of variation the and CH diurnalfurther cycle analysed. of CH We setupspin-up) long simulation for period the (1988–2007, following includinghave reasons: fluctuated two between years (1) 15 of ppb in yr theet 1990s al., 2006; and Rigby 2000s etof methane al., the growth 2008), role and rates (2) ofand we emissions would transport (using like model to a characteristics, obtain set(STE) a such of and better as the six understanding the IH CH exchange stratosphere-troposphere rates exchange on CH a 26 % decrease into global 40 %, burden when of thekilogram OH radiative forcing basis, can due is lead to integrated to CH over 100 an yr increase (Shindell in et al., GWProle 2009). for of CH transport, emission distribution and chemical loss in simulating the interhemi- cus on ozone (O is treated as a tracerand with references a therein). prescribed concentration evolutionCH Note (Stevenson that et most, al., 2006 iffrom not 6.3 all, to full 12.5pating chemistry yr models due models (Stevenson do to et not large al.,is treat 2006). range required of A for more calculating simulatedon conservative the OH estimate climate global concentrations of change, warming in CH or potential the developing (GWP) e partici- for CH TransCom project since the early 1990s foras the sulfur non-reactive tropospheric hexafluoride species, (SF such ning et al., 1999).simulation of Convective Radon parameterizations ( inJacob et CTMs al., have 1997). been The tested full through chemistry model simulations of reactive species with a fo- 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | D to 1 (2) (3) (1) 3 (R1) (R2) (R3) (R4) (R5) (R6) CCl 3 (R4–R6) are decline since 3 3 ) of CH 1 (Similarly, the an- field of the mass- CCl CCl − 3 i 1 3 ) − s i MCF /T ) , 8 D radicals in the strato- − av 1 /T J i 10  ) 1520 × − dt /T ) from ACTM. Modelers were 1775 ; units: m s × 3 − timestep (s), and subscript 0 in- exp( 1 Cl] are provided, which are based = CCl dz 1280 3 3 i,j,k 12 (R1–R3) and CH × − exp( × dt − CH 4 3 i Cl 12 CCl k 10 ) are taken from the JPL synthesis of − 3 10 CCl exp( k − + × 3 oxidation due to stratospheric Cl and O 10 12 D value of 7.959 4 10 CH − 64 1 × ). field to match the ) . 1 × due to solar UV radiation are provided from 6 ) O 1 18774 18773 10 − − 45 5 . s = . × J MCF × , 2 1 MCF 1 D 10 3 10 av J O OH . i,j,k = = − × ( J can also be reproduced for a longer simulation pe- k k 7 D h 1 3 OH M = 10 ) 11 deposition O reactions with OH, Cl and O k . × Cl k × h − 2 Oceanic CH ) 4 k h CCl  h × 3 3 i,j,k ( −→ CCl dt exp 3 i,j,k M ( O − 3 CH × 2 0 P H ) i,j,k CCl 3 HCl 3 Products exp( + + 3 × CH Products 3 OH CCl 0 J Products deposition −→ k , the photolysis rates 3 3  CH 3 D ) denotes the air mass in gridbox ( CH P CCl 1 −→ i,j,k 3 O atmospheric lowest layer depth (m), Rn −→ OH k (CH OH OH −→ Cl k CH CCl = = k J 3 −→ = i,j,k 222 3 + + D ( 1  3 3 3 3 dz CCl M OH O Cl = 3 + + + CCl CCl CCl CCl CH 3 4 4 4 3 3 3 , Rn Radon decays in the atmosphere with a half-life of 3.8 days, and this decay is calcu- For CH The monthly deposition velocities (deposition The tropospheric OH field is reduced by 8 %, an amount that was required to opti- av where, dicates initial concentration. lated in the model at each222 timestep, following J ACTM (Patra et al.,between 2009a) models, Because it the isvalue. resolution necessary This in to value scale the is the calculated stratosphere stratospheric by varies mass-weighted loss widely averaging: of MCF to a common ocean surfaces areshould provided be (Krol applied in et the al., model as: 1998;CH Kanakidou et al., 1999). This sink the NH/SH OH-ratio in(Krol full et chemistry al., unpublished modelin data, simulations Shindell et 2008, varies al., based between 2006)using on 1.1 we their the encouraged preferred and OH modelers model 1.5 field, to intercomparison e.g. submit obtained described another by set a of full simulations chemistry version of their model. required to scale theirweighted interpolated annual and global mean nual and global mean ratecombined constant is for rescaled CH to 2.069 about equal OH abundance in the NH and the SH (Spivakovsky et al., 2000). Since Here, CH The following chemical removal reactionsprescribed for in CH the forward simulations. CH CH CH CH 2.1 Photochemical and surface loss processes CH whether observations of CH riod, i.e., 1990–2007, by TM5 and a variety of other models. The supplied OH field has The temperature-dependent reactionchemical rates kinetics ( (Sander et al., 2006).for online The monthly-mean calculation OH in fields the arespheric model provided (Spivakovsky here by combining et the al.,spheric semi-empirically distributions. 2000) calculated tropo- and For 2-dimensional CH sphere, (2-D) parameterized loss model rates simulated [ on strato- the Cambridge 2-D model (Velders, 1995). mize the agreement between the2000 TM5 (Huijnen simulated and et observed al., CH 2010). The model simulations performed here allow us to verify 5 5 20 15 10 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | con- 3 emission CCl 3 ◦ 1 × were considered 4 and CH , and in Fig. 2c for 4 6 , SF 4 ), where 1 . CH 3 ) are distributed over the coastal 1 − CCl 3 yr 4 oxidation and vertical transport in the 3 emission scenarios are shown in Fig. 2a. 4 CCl and CH 3 6 18776 18775 10 Tg CH ∼ , SF 4 and CH tracers listed in Table 1: 4 http://edgar.jrc.ec.europa.eu 4 5 yr), several years of spin-up are required to establish re- vertical profiles throughout the model atmosphere. A set of ∼ 3 CCl 3 maps from the Emission Database for Global Atmospheric Research . The following source and sink components of CH ◦ 3 1 × CCl ◦ 3 and CH Rn is the radon mixing ratio at all gridpoints. This setup follows the recom- 4 222 wetland emission module ofEcosystEms the (ORCHIDEE) ORganizing Carbon terrestrial andThis ecosystem Hydrology model model in uses (Ringeval satellite-derived Dynamic area et(Prigent of et al., inundation al., 2010). for 2007). thematch The period the emission of wetland is 1994–2000 emission scaled componentis by in used a CYC for multiplication NAT. the An factor rest average of of seasonal 0.76 the cycle to simulationSecond periods set of (1988–1993 wetland and and 2001–2008). tion rice Integrative emissions Simulator (IAV for WLe) Trace is gases obtained2010), (VISIT) from terrestrial which the ecosystem calculates Vegeta- model (Ito, inundated(Mitchell area and based Jones, on 2005). analyzed The rainfall, rice temperature and wetland emissions are scaled by 0.895 (van der Werf etaverage al., seasonal 2006). cycle isemissions, Since used global this total for dataset IAV the BBin is 1988–1996 CYC emissions available NAT. Thus period. are only the lower CYC afterTable than BB Unlike 1). 1997, that emission the is those an This only wetland incorporated partially methodologyand replaced to is by underestimate likely IAV the BB to emissions (see from double closed count burning. some of the open burning based on the GISS inventorythe (Matthews emissions and Fung, due 1987; toemissions Fung et are rice al., scaled paddies 1991), as and arepogenic, in taken emissions Patra from from et Yanits al. rice et seasonal (2009a). cultivation al. cycle are Though is (2009).sions included predominantly controlled due by anthro- in to All seasonal oceanic this these exchange rainfall ( region category and (Lambert temperature. because and The Schmidt,emissions emis- are 1993; based Houweling upon Etiope et and al., Milkov (2004). Wetland 1999) emissions with and interannual variation mud (IAV WL) volcano have been derived from the and 0.69, respectively, to match with CYC NAT. Biomass burning emissionsDatabase (IAV BB) (GFED are version taken 2), from representing the mainly Global forest and Fire savannah Emission burning Interannually varying anthropogenic emissionsmean 1 (IAV ANT) are(EDGAR; based version on 3.2/FT) annual (Olivier and Berdowski, 2001). The combination of dif- Anthropogenic emissions (IAV ANT E4) aredatabase based on the (version more advanced EDGAR 4.0) ( ferent emission categories and the inter-/extra-polationfor of the EDGAR years emission 1990, maps 1995, 2000 are described elsewhere (Patra et al., 2009a). Cyclostationary natural emissionsnatural (CYC wetlands, NAT), such domestic as and those large-scale from biomass all burning, types and of termites are maps are available for eachfor year the until 2006–2008 2005. period. The 2005 emissions were also used and CH 6 Due to long timescales of CH 5. 4. 6. 1. 2. 3. Annual total emissions timeSF series are depicted infor the Fig. emissions 2b of for the CH six CH 3-D initial conditions, prepared followingavailable a for 10-yr 1 spin-up January simulation 1988centrations by for CH ACTM, at is South made PoleJanuary (SPO) 1988. are Radon 1655 will ppb, beits 1.95 spun-up ppt initial quickly due concentration and to is 130 its ppt, set half-life to respectively, of zero. for several days. Hence, 2.2 Fluxes The typical seasonal variations of the six CH where mendation of World Climate Research Programme (Jacob et al., 1997). stratosphere (age-of-air alistic CH 5 5 15 10 25 20 15 10 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | N ◦ and CTL, DOH 20 4 − . In the S–60 ◦ ine mod- ◦ 4 10 emissions 1.25 (Rot- ffl × × 6 × , and synop- for CH 1 6 4 , and 1.66 ◦ latitude to 6 × are taken from EDGAR 4.0 in 2007. ◦ . 1 measurements at surface sites 1 1 − for 60–70 2 − × OH (Patra et al., 2009a,b), CAM 1 ) are simulated using the following yr 3 − longitude 4 s ◦ 2 1 inversion (Bousquet et al., 2006). The CCl − 3 × 4 D) fields) have submitted simulation results 1 erent combinations of emission fields (tracer over 1990–2005 (8675 Tg CH mol m ff 18778 18777 4 23 − . 10 concentrations using the LMDZ model for the period 1 emission distribution is taken from EDGAR3.2 and 4 in 1990 to 6.79 Tg yr × − 3 1 Radon and CH − for land and ocean grids, respectively, within 60 1 CCl 222 mol s − 3 , 8.23 , 6 s ◦ 6 − 2 emission distributions at 1 − 10 6 erent OH, Cl and O( , respectively. After 2002, the regional emission trends follow an ff on average). Only the IAV INV tracer total emissions are slightly × 1 emissions for the di − ). 1 4 4 − mol m 23 − 10 × and 16.1 Gg yr exponential decay with a timescale of 5 yr (Krol et al., 2003; updated). The annual mean CH linearly corrected for the globalthe totals period following 1988–1998. McCulloch Emissions and for Midgley 1999 (2001) to for 2002 are taken as 27.5, 26.0, 17.7, The soil sink represents aderived climatological average from year, the accounting for LMDZ seasonality, atmospheric CH Annual mean SF (2009) for theal. period (2010). 1988–2005, and Theincreased the 2005 from global 4.77 distribution Tg totals yr is are used scaled fromRadon to emissions 2006 are Levin onwards. constructed et basedcell; 0 on SF poleward the of surface 70 type8.30 in each model grid- (Jacob et al., 1997).a global total Radon source, emissionproximately but 2.2 fields are were expected not to to produce a be global rescaled radon to source match of ap- Inversion-derived emissions (IAV INV) are obtained byreproduce optimizing the surface measured fluxes CH to of 1988–2005 (Bousquet et al.,2006–2008. 2006). An average seasonal cycle is repeated for global total removal amounts to 27.21 Tg CH Concerning the wind field and other meteorology, all models, except ACCESS, used Details of individual transport models can be found in the following references; AC- Three other tracers (SF 3. 2. 7. 8. 1. (Pickett-Heaps et al., 2011;man Fraser et et al., al., 2011), IMPACT 2004), and IMPACT LMDZ (version 4; Hourdin et al., 2006), MOZART (version 4; meteorological fields from weather forecast modelsels) either or by by interpolation (o nudgingMost towards models horizontal winds generated (U,which output provided V) output as and as temperature 1-hourly 3-hourly (online averages. averages, models). except LMDZCESS and (Corbin MOZART, and Law,(Gent 2011), et ACTM al., and 2009), ACTM CCAM (Law et al., 2006), GEOS-Chem and GEOS-Chem The model horizontal resolutionvertical, variedfrom 19 1 to 67 levels(resolutions, were meteorological employed. fields) Salient are given featuresdo in of not Table each 2 automatically model for linked configurations guidancein with purpose this only, model and study. performance as evaluated for various features Twelve chemistry-transport models and four ofolution their and variants 2 (2 using at di higherfor horizontal res- the period 1990–2007LMDZ, (Table PCTM, 2). TM5) alsoment, Half participated where of they these in were modelstic the tested and (ACTM, previous diurnal CCAM, for TransCom scale continuous interhemispheric IMPACT, (Law variability et experi- transport al., using using 2008; continuous Patra SF CO MOZART, et NIES08i, al., TOMCAT) participated 2008). for Six the other first models time (ACCESS, in CAM, a GEOS-Chem, TransCom experiment. 2.3 Participating models and output The integrated CH 1 to 6 inwhich Table 1) is agree 542 Tg within yr lower 3 Tg (8641 CH Tg CH fluxes: 5 5 20 25 15 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3 CCl 3 4 ppt in ∼ ` ere et al., , and CH 6 erence of ff , SF 4 ) for NOAA sites. Those concentrations as deter- 3 are in excellent agreement ) using (Patra et al., 2009b ) networks are used in this s 6 E CCl http://www.esrl.noaa.gov/gmd/ 3 W, 10 m) site are used as a re- ◦ annual mean concentration time scale.html 6 S, 26.5 ) and SH ( ◦ n E distribution simulated by the models, a clima- 18780 18779 4 from this are also used for validating simulated 4 http://www.esrl.noaa.gov/gmd ) and the NOAA Earth System Research Laboratory, ) is estimated from the SF 0.01 ppt, compared to repeatability of the analytical system are given in Prinn et al. (2005) for AGAGE and NOAA2003 ex ± 3 τ experiment archived model simulations for 18 yr and 9 tracers, 4 CCl 0.02 3 ). The AGAGE and NOAA scales for SF = vertical profiles measured by the ACE-FTS (Atmospheric Chemistry Ex- 1 (Krol et al., 2005), TOMCAT (Chipperfield, 2006). 4 × , the NOAA4 calibration scale (Dlugokencky et al., 2005) agrees within 5 ppb 1 4 are based on Rigby et al. (2010) and NOAA6 ( scale.html 6 As a check for the stratospheric CH For this analysis, the models were sampled within 1 to 3 h of the sampling times of the The model outputs are extracted for the corresponding sites and sampling time from For CH The TransCom-CH http://www.esrl.noaa.gov/gmd/ccl/scales/CH3CCl3 The Pearson’s moment correlationbetween analysis the is model performed and observed toations time evaluate at series the the for 8 agreements seasonal selected cycles sites. and interannual vari- 2.5 Calculation of IH gradients, IHThe exchange IH rate, exchange and rate atmospheric ( series lifetimes and the ratioand of references emission therein): in the NH ( measurements of stratospheric CH vertical gradients in the tropical stratosphere (100–10 mb). measurements. Time series were constructedverification of of model monthly simulated or seasonal annual cycles mean and interannual samples variability, for respectively. (Paul Krummel and Tim Arnold, personal communication, 2010). tology of CH periment - Fourier Transform Spectrometer) instrumentin onboard the the upper SCISAT-1 satellite troposphere and2008). stratosphere altitudes Please has note been that usedregion the (De is Mazi data sparse. coverage ofUpper The the Atmosphere Research HALOE/UARS ACE-FTS Satellite) instrument (Halogen (Park in Occultation et the al., Experiment tropical 1996) onboard had the a denser coverage and ccl/sf6 (NOAA–AGAGE of 0.04 ppt) (Rigby et al.,1992 2010). reduces linearly The to systematic around NOAA–AGAGE di mined 0 ppt from in co-samples 2001 measurements for the at CH CGO, SMO and MHD by both the networks for SF tion scales for CH ( the hourly surface data files.BRWOCN For the and ACCESS CGOOCN and sites, CCAM models, thebaseline we conditions. nearest have ocean chosen The the grid results ofplacement to for HBA the the (75.6 site, SPO to site foradvice. better PCTM. represent These selections are made as per the modeler’s with the AGAGE/Tohoku University calibration scale (Aoki et al., 1990). The calibra- available for most of theseculated sites. Monthly from or continuous annual (hourly meandata observations averages) available have or from been the flask cal- World2010). sampling Data (events) NOAA Center flasks measurements for are Greenhouseis usually Gases sampled usually website under onshore (WDCGG, clean flowflagged air at to (or remove coastal baseline) local conditions; sites. and this regional The pollution events. AGAGE continuous records have been study. These sites allcovering the have 1990s simultaneous and measurements 2000s of (Table CH 4). Unfortunately, radon measurements are not and TM5 Emmons et al., 2010), NIES08i (Belikov et al., 2011), PCTM (Kawa et al.,sampled 2004), at TM5 280 surfacetroposphere) sites at and hourly 115 timelevels vertical for intervals profile monthly-means for as sites 1990–2007, (at well and all as noon-time daily model 3-D values2.4 levels output for within 2001–2007. at 17 Observational standard data sources pressure and processing Selected sites from thehttp://agage.eas.gatech.edu Advanced Global AtmosphericGlobal Gases Monitoring Experiment Division (AGAGE; ( 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (4) (5) and 6 is IH con- meridional N) are cal- CTL tracer s ◦ distributions 4 Rn, SF 4 − n 3 c 222 ∆ N–30 CCl ◦ 3 isopleths in the height , respectively, at yearly 4 s , respectively, (ideally ver- c . However, two main cate- 1 , average concentrations of 3 − B and CCl ppb n 3 3 c are shown as IH gradients of each ) using the mass balance equation s τ CCl c 3 − and CH n 4 c lifetimes (  , CH 18782 18781 n 3 6 erences are much less distinct at the mid and dt ff dc erent, however, is the transition between the tro- CCl N) and northern extratropics (20 ff measurements at ALT are not available after 2005, 3 − ◦ s 6 E longitudes, and zonal mean CH ect the simulation of tracer gradients in the upper tropo- ◦ dt and 23.689 Gg CH dc ff 1 s n with the lifetimes calculated using Eq. (5) suggesting that − E E and CH 3  4 / ppb 4 CCl 3  72 ppb for the models and 225 ppb for HALOE in the height range for 8 sites are multiplied by the concentration-to-mass conversion E and 180 1 ◦ τ ± B 3 + ). Because SF s n − dt E E CCl with increasing height, respectively, in the height range of 100–50 mb. E are the annual total atmospheric burden, surface emission and photo- 3  4 = s are the average concentrations of SH and NH sites, and L − L n n c − c ∆ E and along the 70  and CH = E 4 4 = and In the troposphere, vertical transport of the NH emission varies between the models, We also estimated CH , s ex dt dB gories of models can berange identified between based 350 on and the 200 density mb. of CH The position and concentration gradient across the gradients between tropics (Eq–10 culated to be 140 of 70–30 mb. most prominently in theTable tropical 3 region, shows mean where vertical deeplatitude gradients cumulus ranges. and convection between is model prevalent. Modelhigh variabilities latitudes to in than three model in broad the di tropics for SF (e.g., Hossaini et al.,along 2010 and the references upward therein). transportin The branch the “tropical of models pipe” the (Plumb, thanACE-FTS. Brewer-Dobson 1996) As in circulation a the appears result, “leaky” limb-viewingto the remote the simulated observations sensing concentration with observations isopleths increasing latitudes appear by in flatter HALOE both compared and hemispheres. The CH 08i and CAM/MOZART/CCAMrates models of show CH theCAM, maximum CCAM and and minimum MOZARTthe decrease models NIES have model employs only isentropic fewlation coordinate of vertical system models layers in is the above known stratosphere.sphere/lower 100 to mb, The stratosphere a and formu- region.uses A isentropic version coordinates in of the the stratosphere,a produces TOMCAT/SLIMCAT model, stronger more tracer which realistic gradients and Brewer Dobson circulation than the p-coordinate version used here in the troposphere and(please lower refer stratosphere to to Figs. representative S1–S17 ACE-FTS for measurements individual model comparison plots of corresponding to the years 1994scale and 2005). features, Generally, equator-pole all models latitudinalstratosphere. exhibit similar gradients, large- Most and significantly verticalposphere di gradients and in stratosphere and thein the lower the heights models at (the which green-to-blue shaded the regions). vertical gradients The maximize ACCESS/GEOS-Chem/NIES- CH within 0.01 yr for CH the simplification is acceptable. 3 Results and discussions 3.1 Zonal mean concentrations Figure 3 compares the latitude-pressure variations of the zonal mean CH CH factors of 2.845 Tg CH tical distribution properties ofair each mass species factors), for should this bethe calculation. accounted period However, we for of note with 2000–2007 that appropriate the calculated average using lifetimes over ACTM simulated gridded loss rates agree c centration gradient. We used twoestimate sites the in hemispherical the NH averagetime (BRW, concentrations MLO) intervals and ( SH (CGO,BRW SPO) is to chosen for thisspecies. analysis. The values B chemical loss, respectively. As approximate estimates of τ 5 5 25 15 20 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | OH, ). This . Gen- erence 28.6 yr) 3 ff 21 ∼ − CCl Rn concen- experiment, 10 3 4 × 222 emissions were 12 3 > erences are caused 3 ff CCl erences between sim- 3 ff CCl 3 erence ff erences remain at that level. ff and CH 6 concentrations become very small to- concentrations compared to the multi- 3 concentrations among all models, while , SF at two selected sites (MLO and CGO). 3 4 4 erences in the simulated concentrations 3 ff CCl CCl 3 erences in time until 2006, and ACTM Rn concentrations compared to the lower tro- 3 CCl ff 3 18784 18783 Rn concentration di 222 222 concentrations (such as MOZART, CCAM). For ex- between 850 and 200 mb), and relatively weaker in 4 lifetime of less than 5 yr, these di 21 and CH Rn distributions over the South Asian monsoon region 3 − 6 222 10 CCl at MLO. However, it is interesting to note that GEOS-Chem × 5.8 yr) (estimates from ACTM simulated loss rates at model 3 , SF 3 4 ∼ , respectively, both the mean values are lower for ACTM (Prinn 5 4 5.63 . . CCl 0 0 after the late 1990s because the concentration gradients across 3 ∼ + − 3 surface emissions and STE are less certain than for CH erences in 1990 persist until the end of the simulations (LMDZ MCF simulations are of an intermediate level of uncertainty, as this species ff 4 3 CCl ers considerably among models. These model features are also present 3 and 6 ff and CH Rn concentrations in the middle-upper troposphere (i.e., smaller di vertical distributions. The penetrative mass flux due to deep cumulus con- simulations suggesting the predominant role of transport in the simulation 15 11 CCl 4 3 + − is considered, which has no chemical loss (middle row). Typical model be- erence of due to photochemical removal is much longer in the stratosphere ( 4 6 222 ff 6 3 is the most complicated species considered in the TransCom-CH sets between models can be explained by initial values assumed by each mod- E during boreal summer (Figs. S2–S5). It has been shown in Patra et al. (2009b), erences in transport and removal rather than the initialization. ff 4 Annual means: 1990–2007 ◦ ff CCl 3 CH The CH (with TransCom OH) calculates the lowest CH being the only outlier) evenwards though 2007. the CH Given the CH by di because the CH erally, the models that simulatemodel lower mean, CH also yield lowerample, CH the high andof low-resolution both TM5 CH simulations show the highest concentrations mates, 38 et al., 2005).centration Despite at the 1 fact Januaryare that 1988, found the significant already models di measurement for were di 1990, initialized using after the two same years con- of simulation. The established model- increasing, followed byproduction/use near-exponential by decrease the Montreal due protocolCH to (WMO/SAOD, 2003). stringent Because the restriction lifetimesthan of of in the its troposphere ( grids), the troposphere tobudget stratosphere of transport CH plays athe minor tropopause role reduced to in less thesphere than global 10 and ppt total (ref. tropospheric Table 3). losses These lifetimes are due to although strato- within the range of independent esti- quate also for independent state-of-the-art transport models. is emitted to the atmosphereHowever, uncertainties by remain a for relatively its well losstolysis quantified by in industrial reaction the use with stratosphere OH as in and a the solvent. the troposphere, rate pho- of STE. Until 1990, CH estimated by Levin et al. (2010) and later confirmed by Rigby et al. (2010) are ade- sion for 2006 andcompared 2007 to and observations produces after a 2000. slower increase This in suggests the that model the concentration global total emissions Figure 4 shows the time seriesulated of and annual observed mean concentration CH di First SF haviour is similar atfrom all the 8 measurements sites. by 0.2 pptThe For in o most 1995, models, after which theels. di simulated concentrations Only divert ACCESSwhich shows uses EDGAR4.0 increasing emissions di without scaling between 1988–2005, and 2005 emis- lations. This is suggesting that someresolution convective models processes that are being are resolved not in present higher in the lower3.2 resolution models. Model-observation comparison of CH is clearer from the simulated at 70 based on ACTM simulations withmulus and convective without cumulus transport parameterization, is thatthe the deep upper main cu- troposphere (seen cause as for higher posphere). rapid The vertical higher horizontal transport resolutionin versions of of higher tracers both IMPACT to and TM5 resulted between 850 mb and 400/200 mb) compared to their respective lower resolution simu- in the SF of the CH vection in tropical latitudes istration strongest di in ACTM and GEOS-ChemNIES08i, (low PCTM and TOMCAT (high tropopause di 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 6 4 4 in 17 8). 1 . − − 0 cients, r > ffi seasonal 12 to (except for 6 − flux season- 4 0.2 ppt yr 4 ∼ in 2006 (please 05 in two tailed . 1 0 − cients greater than = ffi erences in seasonal- P ff , whereas the opposite 3 , and 1996–2007 for SF erences between models inversion used data from in 1997 to 3 ff 1 CCl 4 DOH or the higher horizon- ) of 0.3 for the SF − 3 0.25 ppt yr INV simulations consistently 1 do not always improve the r CCl highlighting the role of meteo- 3 ∼ 4 × 6 1 loss due to the reaction with OH in erence between annual mean con- erent. LMDZ is among the models budget di ff ff 4 cients ( 05 for 18 and 12 data points, respec- seasonal cycles. Because ACCESS 4 . 4; significant at ffi 7 for 5 sites). The length of time series and SF 0.25 ppt yr . 0 3 . 0.04 ppt) at ALT, MHD, SMO, CGO and 0 4 0 ∼ = > ∼ CCl P erences also play a role. r > r 3 ff 18786 18785 seasonal cycle depends strongly on the imple- 1.25 and TM5 EXTRA results are next best in comparison with at MLO, where the measurements show unusual 4 × sets corresponding to the period 2002–2003 have 6 4 cients; ff OH and GEOS-Chem 1 tracers. The corresponding correlation coe ffi cients at all the NH sites (ALT, BRW, MHD, MLO and 4 ffi growth rates (not shown) hovered around 0 to 5, levels are not distinctly di 3 erent CH 3 ff CCl at three selected sites (MLO, SMO and CGO) for the period 2002– 3 CCl 3 3 cients are highest at remote SH sites, CGO and SPO, compared to (Fig. 4). These contrasting behaviors among models for various CH ffi simulations clearly suggest that CH 4 CCl because the emissions are weak in the 2000s. A more detailed analysis 3 3 3 -test for 24 data points). The fact that the models are able to reproduce , the influence of the surface flux on the simulated seasonal cycles can be t seasonal cycle fairly well at all 8 sites, both in phase and amplitude ( 4 BB, which has 7 yr of IAV, 1994–2000) and CH CCl 3 3 CCl 3 cients at 8 sites), except for SF WL and CH growth rates (not shown) decreased from CCl ffi 4 3 erence between 1999 and 2000 mean concentrations. The simulated and observed 6 6 For CH ff 0.44 and 0.53 are statisticallytively. significant Average at CH centrations for two adjacentdi years. The growth rateSF at January 2000 is2000. shown Afterwards, as the the growth raterefer steadily to increased Table 4 to for correlationconsidered coe for the correlation calculationCH of the IAVs is 1990–2007(the for period CH when observations are available). The correlation coe for CH of seasonality at a larger number of sites will3.2.2 be conducted in Interannual a variability future (IAV) study. We calculated growth rates for all tracers as the di agreement between model and observationsThese compared results to suggest the a need defaultality for implementation. improving in our the understanding Northern ofrepresented the Hemisphere as CH land seen regions, in(blue noting the line) that simulated was the not CH OHcycles run loss are with not is analyzed as realistically good windsrology as and other in temperature, models simulating the for tracer CH simulated concentrations. seasonal The role of meteorology is less pronounced tal resolution versions of IMPACT RPB), which was expectedthese sites because for the flux atmospheric-CH optimization.measured CH seasonal cycles forcorrelation coe the NH highall latitude other sites. sites. The For use any of given ACTM tracer, the produce higher correlation coe listed in Table 4, suggestmented that wetland the and CH biomass burning fluxes. The CH contrast, the clear seasonalitiesSPO (amplitude are well reproducedStudent’s by the models ( the observed seasonal cyclesnevertheless indicates provide useful that information even forCH though model validation. the All signals models are reproduce the weak, they studied using the 6 di been subtracted from the monthly-mean values.in All the models capture seasonal the cycles salientcoe features at very high statisticalfluctuations and significance a (ref. large Table data 4erage gap (2005–2006), for during low 2002. correlation average Even correlation for coe cycles the at years MLO with are dense obtained data due cov- to a very small seasonal cycle of less than 0.04 ppt. In 3.2.1 Seasonal cycles Figure 5 shows modelSF to measurement comparisons2003. of These the sites seasonal have been cyclescontinental selected because of emissions of CH for their each locationity, at of approximate large these linear distance species. trends of and the To o highlight di that most strongly underestimate the observedis level true of CH for CH and CH the troposphere is not theand only that control other on factors the such CH as transport di the simulated CH 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 9). . 0 growth r > 4 CTL sim- 4 BB) improves transport and CTL. However, 4 emission simu- 4 growth rates for 4 4 4 emission increase of from the troposphere ˜ no induced higher air CTL emission and OH ˜ no event (Langenfelds 4 CTL lifetime (9.82 yr) is 4 4 4 from 1990 to 2000 for sim- 4 growth rate at BRW did not 4 EXTRA displays a large positive 4 in the NH, tropics and SH, respec- 1 BB), the correlations tend to deteri- − loss due to higher air temperatures. As emissions. 4 WL 4 4 EXTRA produces better model-observation 4 18788 18787 IH gradient within the measurement accuracy cients for the growth rate IAVs in Table 4; see growth in the late 2000s have drawn consider- 0.18 yr (for all models and years), which is an ffi 6 4 ± emissions are of purely anthropogenic origin, their OH reaction rate as modified by CH 3 + 4 emissions by about 40 Tg CH CCl 4 3 ) of 1.39 BB, only CH chemical destruction and emissions during the early 2000s ex 4 4 τ growth rate in the early 2000s using the CAM-Chem model. Their measurement precision). These gradients translate to an aver- during 1991, 1997 and 2007, respectively, with gradual changes and CH 4 × 1 6 − 2 during 2001–2007, synchronized with the Chinese economic growth, √ 1 − yr 4 2.5 ppt yr ∼ − 0.057 ppt ( ± The decreasing growth rate in the 1990s, near zero growth rates in early 2000s As seen from Table 4, inclusion of biomass burning emission IAV (CH Figure 6 shows the model-observation comparison of the IAV in CH 4 Tg CH indication of close model-modelsmaller agreement Fig. compared 8. to the This model model-model pool spread of is the much 1990s (Denning et al., 1999), which gave models are useful for the verification of emission inventories. 3.3 Interhemispheric gradients and exchange rates Figure 7 shows theobtained concentration using annual gradients mean between observedthe two and TM5s, NH modeled time sites simulate series. and theof All two observed models SH except SF for sites age IH exchange time ( (Dlugokencky et al., 2003).∼ The EDGAR4.0 anthropogenic CH produces inconsistencies between observed and2007 simulated (thus growth rates the during lowest 2003– also correlation Fig. coe S20). This indicates that forward simulations using multiple forward transport in coarse resolution global models (Patra et al., 2008). and the reappearance ofable positive interests CH for developing emission inventories.suggested For a example, decrease Lamarque of et CH al. (2010) ulating the zero CH estimate is largely inconsistent withis our results, achieved which between is CH suggesting that a steady state a consequence, these emissions result inobserved an at excellent the agreement with SH the andstrong growth NH in rates sites 1998). (except The thatcorrelate IAV the significantly of simulated with the tropical multi-model the signalAlaskan average observed is wetland CH a IAV region, bit because and too this the site site representation is error located is close large to for the the coastal sites these emissions compensate for the extra CH anomaly on top of the emissions from biomass burning during 1997/1998. Combined, temperatures (Reaction R1), resultingand in thus faster a decrease removal in ofestimated the CH to growth be rate. the Indeed, shortest the among 1998 all CH simulation years. the IAV model-observation agreementwhen wetland at emissions all are sites included compared (CH to CH et al., 2002). Duringtions show this a event decrease the inconcentration observations the (both growth show without rate. an IAV) Interestingly, cannot the increase, explainattributed CH while this to the model the behavior, simula- which increased istemperature CH therefore in the model. This, in turn, is caused by the El Ni lated by the VISIT ecosystem model included in CH three broad latitude regions: theTRA NH, emissions tropics (refer and to SH Figs. corresponding S20 torates and the for S21 CTL the for and others). 1990s EX- are Averaged observedtively. 5.25, Almost CH 5.06 no and increase 7.01 ppb in yr Additionally, concentration it is observed can for be the seencompared 2000s that to (except the for the tropical 2007). IAVs and in SHulations the sites. capture growth Figure the rate 6 observed shows areis that reduction higher although not in at the well the CH the reproduced. decadal NH average sites Most growth prominent rates, is the the IAV 1997/1998 El Ni orate. Compared toagreement CH for growth rates (Fig. 6, right panels). The wetland CH and in between.Because These both temporal SF variationsproduction, are consumption well and simulatedcomparison release to by to the all natural the components models atmosphere of CH ( vary relatively smoothly in 5 5 25 15 20 10 15 20 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 1 4 4 − dur- yr 4 1 − 0.82 and yr 0.10 and 4 − ± N latitudes). ◦ destruction and S–5 4 ◦ gradients (0.13 ppt) erent models are, as 3 ff CCl erences between models 3 ff di emissions (note: this version ex 6 IH gradients (127 ppb compared τ IH gradients are best reproduced 4 4 in comparison with the observations 4 varies between 490 and 509 Tg CH 42 for all models, but increase to 4 . 0 . The CH 18790 18789 DOH, NIES-08i lie above the fitted line for CH IH gradient generally also produces a larger − 3 and the role of transport and CH 4 = at 10 mb; zonal average for 5 6 . r 4 3 CCl 4 3 lifetime in the troposphere is an order of magnitude 4 CCl DOH, models used the same OH distribution. The 3 cient, ffi in the troposphere, resulting in faster CH (ref. Table 2 for time-averaged model specific lifetimes). erent models. Here it should be remembered that all, but 69) are found for the period of 2000–2007 between the CH . ff 4 3 0 − OH version is due to smaller SF = CCl at 100 mb – CH 3 r lifetime due to atmospheric loss processes (Reactions R1–R3) is 4 for interannual variability) years and ranges from 9.50 4 INV emissions: deviations are within 5 ppb for 7 models (Fig. S17). σ 4 of 1.39 yr is in excellent agreement with the observation-based estimates ex 0.06 yr compared 1.2 yr in the TransCom experiment during the 1990s. The τ 0.14 yr for the di ± OH and GEOS-Chem 0.08 (1 ± CTL and CH ± IH gradient, but produces one of the largest CH range of 0.8–2.0 yr. In this intercomparison, the 4 6 Figure 11 shows the temporal variability in the estimated lifetimes (Eq. 5) for All three long-lived species show a similar relationship for the IH gradient: the model 0.61 for 1990s and 2000s, respectively, by excluding NIES-08i). Possible explana- ex ACTM decrease from the 1992–1999 periodeled to gradients remain the remarkably 2000–2007 constant. periodsimulated Based in on concentration all this models, growth analysis the we rates suggest mod- to at that stratosphere the the transport surface rate sites of is CH linked to theCH troposphere The median CH 9.99 10.27 smaller growth rate because CH shorter than thatloose in during the the 1990s, stratosphere.icant but correlations as Although ( the models these attaingrowth relationships their rates are steady and state, again vertical statistically gradients quite signif- in the lower stratosphere. While the growth rates expected, inversely proportional to the calculatedthis photochemical relationship destruction. appears However, loose, particularlysteady for state 2000–2007, (correlation when coe the models− approach tions are investigated instratosphere Fig. (CH 10b, using theModels vertical showing gradients greater in gradientsstronger the have trapping equatorial slower of lower Brewer-Dobson CH circulation, and thus The calculated photochemical lossduring of the CH first eighting years the (1992–1999), last and eighteight-years between years averaged 497 (2000–2007) growth and rates of at 513 the Tg the CH simulation. surface sites Figure for 10a the suggests di that the 3.4 Photochemical removal of CH Taking into account theChem/IMPACT/PCTM and IH LMDZ/NIES-08 gradient showed oflower systematically all IH higher, gradients, three similar respectively, compared species, and to TM5/CCAM, the ACTM/GEOS- observations. at lesser distinction, e.g., GEOS-Chem but lie below theusing fitted the line CH for CH (smaller) IH gradients(Fig. for 9). CH TheSF intriguing exception isto MOZART, an which observed exhibits value of anduring 101 excellent ppb) 2003–2007. and match Similar one contrasting for of behaviour the is smallest also CH seen for several other models forward model simulations. Aboth the tendency observations towards and faster theand IH 0.15 simulations yr, exchange (a respectively) rates between decrease 1996–1999 is inlinked and seen exchange 2004–2007. to time for This the by behavior widening might about2008), of be 0.2 and the the tropical associated weakening beltlived of during tracer the tropical/subtropical transport the mixing between last barrier the three for two long- decades hemispheres (Seidel (e.g., Miyazaki et etthat al., al., produces 2008). a larger (smaller) SF is 0.62 average of 1.3 yr (Gellerestimation et by al., ACTM 1997)used and EDGAR4.0 1.5 without yr scaling), (Levin highlighting and the Hesshaimer, role 1996). of the emission The strength under- in the τ 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 3 CCl 0.3 yr 3 ± for optimiz- 3 0.50 yr, which ± Rn simulated by CCl 3 222 observations are less 3 and intercomparison experi- ). 6 4 CCl 3 0.15 yr (this study based on erences in concentrations of 4.9 , SF ff ± 3 3 Rn simulations and comparisons for the 1990s and 2000s, respec- CCl 1 0.02 during the 1992–2007, 1992– CCl 3 222 − 3 ± growth rate, by focusing on the results methane.php yr 4 4 concentrations using TCCON observa- , CH vertical profiles measured using aircraft, 4 4 . 4 3 18792 18791 CCl 3 14 Tg CH ± lifetimes during the 1990s is an order of magnitude 0.18 and 4.62 3 ± dropped significantly, less than 0.4 % variability is sim- ort towards ease of access, time series at a subset ff 3 and CH lifetime due to all loss processes (Reactions R4–R6) is CCl 4 3 3 CCl 3 9 and 514 CCl emission time-lines. , CH ). Information on how to access the full dataset is available on 3 ± 6 4 0.13, 4.59 ± erent CH lifetime for 16 models or model variants agrees well with the lifetime http://transcom.project.asu.edu/T4 ff satellite observations in the upper troposphere and lower stratosphere. 4 4 0.17) estimated from the measured mean concentrations at 8-sites. This is ± The vertical redistribution of tracers, basedto on CH Simulated seasonal cycles, by comparingbackground to stations. observed seasonal cycles at remote Inter-annual variations in the simulated CH of six di Large-scale interhemispheric (IH) transport, by comparingIH modeled gradients and of observed SF In addition to the site-specific data, gridded output at monthly intervals at the model 1. 3. 4. 2. http://gaw.kishou.go.jp/ We analyzed concentration time series16 of chemistry-transport CH models asment. part We of focused the the analysis TransCom-CH on the model-to-model di archived for the periodextension 2001–2007. to recent We years will believe belite these useful observations sets for (SCIAMACHY, AIRS, comparing of GOSAT) the and model– model aircraft output simulations HIAPER observations with and Pole-to-Pole (e.g., satel- Observations their HIPPO ofWofsy Carbon et Cycle al., and 2011). Greenhouse Gases Study, 5 Summary and conclusions ( this website ( horizontal resolution at2007. standard pressure Afternoon levels averages are (12:00–15:00 LT archived – for Local Time) the at period daily 1990– intervals are also of surface sites are archived at the WMO World Data Centre for Greenhouse Gases ing tropospheric OH abundance.surement We community. also welcome In use an of e this data set by the mea- For this analysis we used resultswhich of is chemical tracer a simulations very ataddition only small 8 vertical subset selected of sites, profiles thehave of 280 been chemical surface archived sites tracers at forsimulations and 115 which are several sites. output planned meteorological is focusing More on(2) available. parameters analyses (1) analysis In on CH of the verticaltions, basis column of (3) averaged the CH using TransCom-CH increase, decrease and exponential decay of CH when the emissions of CH ulated. This implies that inversions touncertain estimate since OH 2000, from CH a finding in good agreement with Montzka et al.4 (2011). Further work and data accessibility 1999 and 2000–2007, respectively.is TM5 close simulates to a the(Prinn lifetime estimates et of using al., 4.92 the 2005),8 measured 5.0 sites). yr CH (WMO/SAOD, All 2003) otherCAM, or CCAM, models 4.94 LMDZ calculated and MOZART a calculatevariation lifetimes shorter of in 4 lifetime, the yr by estimated or CH shorter. anhigher The average interannual than value that of in 0.3lifetimes yr. the during 2000s. 1992–1999, a We period find with up substantial to emissions. 5 % During variability 2000–2007, in the modeled CH (10.0 because a close balancesurface is emissions achieved (513 between the modeledtively). atmospheric The loss median and CH estimated net to be 4.61 median CH 5 5 15 10 20 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 4 3 at − 3 yr CCl 4 3 CCl 3 0.08 and concentra- ± 4 , also exhibit erences may 6 ff 14 Tg CH were found to . Compared to 4 ± 3 and CH concentrations. . This multi-tracer 4 3 3 ˜ no resulted in larger erent models. The distributions, ranges CCl ff 3 6 CCl CCl 3 3 er in the simulated height ff 9 and 514 erences. Next to the inter- erences play a smaller role. ff ff ± . It was also found that the and CH 4 EXTRA) most successfully sim- and CH seasonal cycles depend on the 4 and CH 4 4 4 4 CTL) without a significant increase 4 growth rate shows (weak) correlations tracers show a higher correlation with 4 4 . However, there are exceptions, which 4 Rn concentrations agree between models, 222 18794 18793 is used to check the consistency of the employed OH 3 ecting the reaction rate) di ff CCl 3 loss due to OH in the troposphere is not the only cause of the global flux representations was used to investigate the role of 4 erences. Thus, horizontal and vertical transport di ff 4 di erences are observed in regions of deep cumulus convection, e.g., 4 ff emissions (soil sink subtracted) of 513 4 usive around the tropical tropopause. ff EXTRA), or less than average OH should have been present in this period. satellite observations in the upper troposphere, most models appear to be destruction, whereas the observations clearly show a rise in CH 4 4 4 erences in vertical mixing of the emissions and in stratosphere-troposphere 0.13 for the period 1992 to 2007. This underscores the fact that OH (assumed ff abundance and distribution. Two modelsprescribed used field. an alternative Generally, OHalso models field simulate next that to a simulate the a lowindicates low that abundance CH abundance of of CH modeled CH CH also be important. for the periodfollowed 1990–2007 by reproduces the the stabilizationemissions from declining in the growth the wetland and 2000s. rateulates forest fires in the (CH Inclusion observed the ofhigher interannual 1990s, tropospheric interannual variations temperatures variation during in the in CH CH 1997/1998 El Ni tion. To match theCH observations, either enhanced emissions areThe required simulation (as of in CH specific processes in reproducing thegrowth observed rate. interannual The variations control inin emission the case CH anthropogenic (CH emissions and no interannual variability in natural emissions Although the simulated zonal mean significant di the south Asian summer monsoon domain.to Unfortunately, check observational the evidence model behavior is lacking. Models also di The IH exchange time,from calculated 1.79 from to themodel-average 1.17 simulated value yr of SF (average 1.39 yr over isservational in evidence. 1996–2007) close Models for agreement that withfaster the earlier show exchange di studies faster (smaller and IH ob- IH exchangeevidence for gradients) provides SF for clear CH directionsthe for the exchange improvement rate of calculatedobservations specific from suggest models. an the acceleration Both models ofto and the the IH the 1990s. exchange rate in calculated the 2000s from compared All the models reproducebackground the sites very observed well. seasonaldestruction The cycles by simulated reaction of CH withemissions. CH OH, Two suppressing of the thethe six observed seasonality seasonal simulated of cycle. CH the underlying A set of six CH The role of removal by OH on the simulated CH of large troposphere-stratosphere concentrationCH gradients of CH too di ± i. v. ii. Finally, the multi-model lifetime estimates for CH 5. iv. iii. We find net CH for the 1990s and 2000s, respectively, to the atmosphere are consistent with the exchange are the mainhemispheric causes transport in of models, the thisarchived model-to-model issue model requires di output. further analysis, e.g. based on the be fairly constant over4.61 the simulation periodconstant in with the simulations) median is valuestransport the driving and of factor temperature in 9.99 (a the budgets of these gases, and that Further analysis reveals that thewith simulated the CH modeled verticalthat gradient in di the equatorial lower stratosphere. This suggests The main conclusions can be summarized as follows: 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 ´ ere, J., doi:10.1016/S1352- , 2011. D and Cl in order to simulate CH 1 er, A., and Snible, W. J.: Halocarbons and ff , 2009. Rice (Xiaoyuan Yan) for emission inventories. 18796 18795 . experiment could not be completed. The ACE mission 4 doi:10.5194/gmd-4-207-2011 emissions using SCIAMACHY satellite retrievals, J. Geophys. , 2006. 4 (UK) with a CSIRO land-surface scheme (CABLE) and the help , particularly Chris Jones and Fiona O’Connor, Martin Dix and , 2006. ff TM doi:10.1029/2009JD012287 This work is partly supported by JSPS/MEXT KAKENHI-A grant number , 1998. WETL (Bruno Ringeval), RIGC ce Unified Model ce and CSIRO sta ffi ffi doi:10.1038/nature05132 doi:10.1029/2005JD006058 its sensitivity to2310(98)00105-8 climate change, Atmos. Environ.,2001 32, using 3293–3299, a 3-D global chemical transport model, J. Geophys. Res., 111, D10307, Kline, E. S., Lafleur, B.Nance, J. G., D., Lind, Neu, J., J.other Lovitz, L., atmospheric S., Romashkin, trace Mondeel, species, P. A., D. Chapter Sche edited J., 5, in: by: Montzka, CMDL S. Thompson, Summary A., T. Report M.,2004. 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R.: Influence of biomass burning 5 5 25 20 10 30 15 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ◦ 1.8 3 × 0.50 0.13 0.15 0.13 0.15 0.02 0.21 0.13 0.13 0.16 0.25 0.18 0.15 0.13 0.3 0.14 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 1.8 CCl 3 ∼ and cycling in the ◦ 4 2.8 × 0.12 4.92 0.110.27 3.77 0.11 4.01 4.70 0.150.06 3.90 0.1 4.75 4.54 0.10 4.60 0.10 4.84 0.070.09 4.54 3.90 0.12 4.71 0.13 4.55 0.11 4.84 0.05 4.63 0.11 4.88 CTL CH ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 2.8 4 ∼ Avg. lifetime (1992–07) vertical coordinates are a hybrid CH η 0.88, and stratospheric OH is taken from AGCM × e IAV Monthly; Partial IAV intercomparison project. See Sect. 2.2 ECMWF, ERA-interim 10.1 NCEP; U, V; SST 9.94 JCDAS, ERA-interim-PBLNASA/GSFC/GEOS5 10.0 10.1 NCEP/NCARNASA/GSFC/GEOS4/5 9.60 10.2 NCEP/NCAR 9.88 ECMWF; U, V, T; SST 10.0 NCEP2; U, V, T; SSTNCEP2; U, V, T; SST 10.0 9.51 NASA/GSFC/GEOS4 9.99 ECMWF, ERA-40/interim 9.98 NASA/GSFC/GEOS4/5NASA/GSFC/GEOS4 9.95 10.1 ECMWF, ERA-interim 10.1 + 4 d IAV WLe Monthly; Full IAV η η IAV BB Monthly; Partial IAV θ - + η σ σ η σ σ η σ σ η η η η + over land and ocean, Annual; No IAV 25 30/47 32 58 28 28 19 Vertical 67 67 30/47 55 60 38 AGCM; SST55 9.93 25 1 − 0.76 IAV WL Monthly; Full IAV 18808 18807 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ c s 2 + 2.0 2.5 1.0 4.0 1.9 1.8 2.5 2.8 2.8 2.0 1.0 2.8 2.5 4.0 1.0 − emissions are used from EDGAR4.0 without scaling the global totals to match with Levin × × × × × × × × × × × × × × Resolution Meteorology 6 220 km 18 0.895 Rice) × ∼ ∼ 1.8 2.8 2.8 2.8 vertical coordinates are pressure divided by surface pressure, ”. + ∼ ∼ ∼ ∼ σ Horizontal are given. IAV ANT EDGAR4.0 Monthly; Partial IAV 3 b + CCl 3 flux components. CTL – 0.35 CYC BB BB – CYC WL BB – CYC WL – Rice 4 4 4 4 CSIRO LSCE 3.75 UoELLNL 2.5 1.25 RIGC Institution CSIRORIGC 3.75 SRON 1.0 (0.69 Wetland ANT EDGAR 3.2 EDGAR3.2 with trends and distributions Annual; Full IAV EDGAR 4.0 (@JRC); Global totals modified Annual; Full IAV and CH DOH 4 a 1.25 $ × E4 CYC NAT 1 1 BB CH OH × 3 1 5 CTLCTL CYC NAT (CYC BB & CYC WL) INVEXTRA* CH IPSL/LSCE inversion Monthly; Full IAV BBWL CH CCl Overview of participating transport models and model variants, and average lifetimes List of tracers simulated in the TransCom-CH TM CCAM GEOS-Chem ACTM LMDZ ACCESS ACTM IMPACT latitude or distance or spectral resolution indicated by T (triangular) maximum wave number (T42 and T63 for 4 4 4 4 4 4 3 6 tracers × Rn) respectively 4 222 1. CH 6. CH PARAMETERS DESCRIPTIONCH 3. CH TIME RESOLUTION 4. CH 5. CH 2. CH Other tracers 7. SF 8. Radon-222( 9. 1.0 and CH 0.1 atom m (MCF) modified 11a 4 55a GEOS-Chem9 UoE1011 NIES08i PCTM TM5 2.5 NIES GSFC SRON 2.5 1.25 6.0 2a 3 CAM7 8 CU MOZART 2.5 MIT Sl. No. Model Name 1 2 66a IMPACT12 LLNL TOMCAT 5.0 UoL CSIRO: Commonwealth Scientific and Industrial Research Organisation, Australia; GSFC: NASA Goddard Space Flight Center, USA; RIGC: Research CTMs driven by AGCM transport are identified in bold (nudging parameters in right-most column), and model variants are shown in italics. The model Longitude The tropospheric OH field is taken from CHASER full chemistry model [Sudo et al., 2002] and scaled by Terrain-following (height) coordinate system for ACCESS, The source of meteorology (NCEP2 (AMIP DOE II): Kanamitsu et al., 2002; NCEP: Kalnay et al., 1996; NASA/GSFC/GEOS4/5: Bloom et al., 2005; ECMWF: a variants are indicated by post-fixed parameters, following a “ b Institute for Global Change,Climat Japan; et CU: de Cornel University, l’Environnement,University USA; of France; LLNL: Edinburgh, NIES: UK; Lawrencec UoL: National Livermore University National Institute of Laboratory, for Leeds, USA; UK. Environmental LSCE: Studies, Laboratoire Japan; des Sciences SRON: du Netherlands Institute for Space Research; UoE: respectively). d sigma-pressure coordinate (GEOS-Chem hase 30 or 47 layers for 1990–2006Uppala or et 2007, al., respectively), 2005; NIES08i$ JCDAS: has Onogi a et hybrid al., sigma-isentropic. 2007) and(Takigawa parameters et used al., in nudged 1999) AGCMset as are al. discussed given. (2010). in Patra et al. (2009a). SF Table 2. of atmospheric CH * this tracer istion, called but EXTRA included here because since the noperiod other VISIT at bottom-up the terrestrial wetland time ecosystem emission the scenariosearch model intercomparison was Unit protocol (Ito, available time was with 2010) series released. IAV fluxes version for VISITreanalysis the 3.0 are is full (CRU (Kalnay still driven simulation TS3.0) by et under dataset climate (Mitchell al., evalua- variables and from 1996) Jones, the 2005; for Climate updated Re- the values) and periods NCEP/NCAR of 1988–2005 and 2006–2007, respectively, and CH Table 1. for a description of the CH inundated areas is modeled using the scheme of Cao et al. (1998 and references therein). Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | S, N, 1 .1 .2 .2 .2 .2 .2 .2 .1 .1 .1 .1 .1 .1 .1 ◦ ◦ .03 ± N) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ◦ ± .06 W, 90 W, 53 ◦ ◦ − /average, (ppt) and σ 3 3.75 (38 %) 5.91 (13 %) 0.25 (25 %) 6.73 (28 %) 0140 (61 %) .1 .81 .03 .94 .1.2 .95 .50 .2.2 .2 .43 .1.2 .57 .1 .46 .56 .69 .02.1 .96 .1.02 .96 .95 .92 .1 .81 .02 .96 0.018 (14 %) 64.96 (50 %) 0445 (126 %) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± . CCl ± 3 3 N, 11 m), MLO (Mauna : 1996–2007) ◦ S, 42 m) and CGO (Cape 6 CCl ◦ 3 .3.4 .93 .1 .43 .88 .2.2 .46 2 .03 .2 .60 .2 .48 .1 .63 .68 .2.2 .97 .3 .95 .2 .94 .95 .1.04 .83 .95 .2 .98 W, 71 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ◦ W, 14 ◦ N) NH (30–60 ◦ and CH 6 ) between model simulated and r .1.2 .69 .02 .70 .89 .2.3 .38 .1 .24 .2 .54 .2 .53 .2 .38 .73 .1.1 .71 .1 .72 .1 .64 .61 .04 .96 .1 .74 S–15 CTL (ppb), CH ◦ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± : 1991–2007; SF , SF 4 3 4.48 (27 %) 9.79 7.69 (45 %) 44.65 2.73 (32 %) 23.90 4 0.017 (39 %) 0.128 0.148 (56 %)65.07 (52 %) 0.229 128.77 0.203 (49 %) 0.353 0.182 (62 %) 1.01 ± ± ± cient ( ± ± ± ± ± CCl 3 ffi .2.1 .80 .1 .13 .96 .2.2 .34 .2 .05 .75 .2.2 .68 .2 .42 .2 .77 – .34 .1.1 .88 .86 .1.1 .86 .88 .1 .86 .03 .95 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± , CH 4 ± (ppt), CH .06 .02 6 − − 18810 18809 S) Tropics (15 ◦ .1.1 .1 .50 .91 .1.2 .1 .32 .2.1 .47 .1 .38 .2 .27 .51 .70 .1.1.2 .69 .1 .66 .70 .74 .04 .96 .1 .63 For seasonal cycles (2002–2003) N, 45 m), SMO (Samoa, USA; 171 UT/LS gradients ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ◦ ; and between model variability defined by 1 Tropospheric gradients σ N, 3397 m) and SPO (South Pole Observatory, Antarctica; 25 1 ◦ 1.37 (17 %) 16.59 2.57 (26 %) 17.18 W, 13 0.074 (47 %)35.89 (43 %) 0.264 124.04 0.005 (18 %) 0.044 0.708 (65 %) 0.415 0.145 (31 %) 0.295 0.527 (54 %) 8.66 ± ◦ ± ± .2 .68 .1 .75 .2 .79 .2.2 .66 .2 .46 .76 .2.2 .51 .2 .68 .77 .2 .68 .3 .72 .2.2 .42 .92 .2 .73 ± ± ± ± ± ± .02 .87 .03 .95 W, 20 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ◦ ± ± 7.86 9.91 N, 210 m), BRW (Point Barrow, Alaska, USA; 157 .11 .10 .09 − ◦ 0.158 84.45 0.026 0.473 − − − − For interannual variations (CH S, 94 m) sites are operated under the AGAGE network by the Massachusetts Institute of W, 82 ◦ ◦ .2 .23 .2 .67 .1.1 .37 .93 .2.2.2 .14 .2 .01 .1 .40 .2 .47 .32 .36 .3 .3.1 .48 .63 .03 .97 .3 .3 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± E, 41 ◦ .48 .33 .78 .97 ALT BRW MHD MLO RPB SMO CGO SPO ) gradients in the troposphere and UT/LS region for three broad latitude bands, 21 ∗ − E4 .27 E4 .21 3 3 Multi-model averages ( BB .55 BB .38 Average (across all models) correlation coe 10 3 3 Site \ × CCl CCl CTLCTL .52 BBWL INV .69 EXTRA .61 .64 EXTRA .84 BBWL INV .10 .88 CTLCTL .16 erence between: 200–100 mb 100–50 mb 200–100 mb erence between: 850–400 mb 850–200 mb 850–400 mb 4 4 3 3 CCl CCl 6 6 Rn 1.084 Rn 0.984 3 4 4 4 4 4 4 4 4 3 4 4 ff ff 6 6 Rn ( Di SF CH SpeciesDi SF SH (60–30 CH CH 222 CH 222 SF CH CH4 CH CH CH CH SF CH4 CH CH CH CH CH Tracer CH CH ALT (Alert, Canada; 62 ∗ Loa Observatory, Hawaii, USA; 156 Table 4. observed seasonal cycles and interannual variation of CH 2810 m) are managed under theLaboratory NOAA (GMD/ESRL) cooperative network (Dlugokencky by et the al.,25 Global m), 1998; Monitoring RPB Division, Butler (Ragged Earth et Point, System al.,Grim, Barbados; Research 2004), Australia; 59 and 145 MHD (Mace Head, Ireland; 10 Technology (MIT, USA), Scripps InstitutionsCommonwealth of Scientific and Oceanography, University Industrial Research ofgia Organization California, Institute (CSIRO, San Australia), of University Diego Technology, USA of (SIO/UCSD, (Cunnold Bristol, USA), et UK and al., Geor- 2002; Prinn et al., 2000) Table 3. within parenthesis222 in %) of simulated SF namely, the SH midlatitude, tropics and NH midlatitude. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | fluxes for fluxes are 4 3 CCl 3 E4 BB CTL INV 4 4 4 4 EXTRA BB_WL 4 4 Photo- and CH 2. CH transport) 3. CH 1. CH 5. CH

6 surface sinks

6. CH chemical and

4. CH

3 3

4 4

[Gg yr [Gg CCl CH ] ] yr [Tg-CH CH

-1 -1 emission 6 1999 , and annual mean CH 400 200 0 600 580 560 540 520 500 (a) 7. SF (a) (b) ) (for annual mescale/ 20052005 flux 4

interhemispheric (c) 4 CTL CTL_E4 BB BB_WL INV EXTRA 1998 20002000 CH CTMs model simulation experiment. (soil sink included) ( . The annual mean SF 4 18812 18811 Chemistry- (b) 19951995 Transport models 19901990

5 7 6 4

1997

60 50 40 6 ] yr [Gg SF

4 4

] month [Tg-CH CH -1 -1

flux seasonalities are shown in 3 4 TransCom CH CCl 3 Radon emission 222 (for OH emission regional transport) 9. CH meteorology validaon) 8. . (for synopc mescale/ Analyzed, AGCM or AGCM-nudged (c) The average CH Schematic diagram of TransCom-CH Fig. 2. the period of 1988–2008shown are in depicted in Fig. 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) 1 − s 3 − CTL (top 4 for tropospheric 4 ; climatology) and a shows the climatological (a) loss rate (units: molecule cm 4 set is added to the concentrations in each ff ) show CH CTL plots, because soil sink was not accounted d 4 . The black line in 18814 18813 and c (b–o) (bottom row) at two selected sites, MLO (left column) and 3 for two seasons and over two longitudes are available in the CCl 3 3 OH, respectively. An o CCl 3 erences between observed and simulated annual mean CH ff and CH 4 , CH (middle row) and CH 6 6 Annual and zonal mean latitude-pressure (in mb) cross-sections of CH Time series of di Rn, SF row), SF CGO (right column). CAM is excluded for CH Fig. 4. for during simulation.averaged over Time 1000–200 series mbrespectively. and of all annual latitudes/longitudes, mean are values and shown in tropospheric Figs. model S18 values, and S19, tropopause height. The contour lines in ( and lower stratospheric altitudes asas observed simulated by the by ACE-FTS instrument the ( models inas 2000 in the ACTM and ACTM Fig. 3. panel (given after thevalue model name of in222 1770 ppb) ppb that between adjusts the 950 mb model and fields to 500 mb. a common average Detailed model-to-model comparisons for Supplement (Figs. S1–S17). Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 6 in NH 4 tracers are given in CTL (top row), SF 4 4 18816 18815 (bottom row) at 3 selected sites, MLO (left panel), SMO (middle 3 CCl 3 Comparisons of observed and simulated annual mean growth rates of CH Comparisons of observed and simulated seasonal cycles of CH Fig. 6. (middle row) and CH (top row; ALT, BRWand and SH MHD (bottom average), row;EXTRA tropics CGO (right (middle column). and row; Growth SPO rate MLO average) variabilities for RPB corresponding two 4 and selected other SMO CH fluxes, average) CTL (left column) and Fig. 5. panel) and CGO (right panel). Figs. S20 and S21. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | W, ◦ -axis y S, 26.5 ◦ concentrations between 3 CCl 3 and CH 6 , and multiplied by 1.0003 and 1.0333 for 6 , SF 4 18818 18817 ) estimated using the measured and simulated time series of set of 0.02 ppt for SF ex ff τ in NH (BRW, MLO) and SH (CGO, SPO) by employing Eq. (4). 6 IH exchange time ( Interhemispheric gradients (IHGs) for CH and MCF, respectively (see text in Sect. 2.4 for further details). Please note that adjustment 4 average SF Fig. 8. view of MCF for the 2000–2007 period. Fig. 7. 10 m) site is chosen for PCTM due to no SPO data in all files. Inset shows expanded of the AGAGE data to NOAAbecause scale their is data made coverage is just most for complete convenience. during These 1990–2007. 4 sites Haley Bay are (75.58 chosen here CH the NH (BRW, MLO) andNOAA SH scales (CGO, by SPO) adding sites. an The o values at CGO (AGAGE) are adjusted to Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | OH is are depicted, 3 DOH are excluded CCl 3 EXTRA) with their simu- 4 and CH 4 TM5 TM5 (b) N (a) o NIES-08i ACCESS ] S-5 800 -1 o ACCESS 0.36 yr 4 TM5_1x1 TM5_1x1 GEOS-Chem PCTM CCAM NIES-08i ACCESS OH and GEOS-Chem 600 TM5 NIES-08i CCAM CCAM NIES-08i ACTM MOZART y=15+316.49*x (r=0.83) y=-0.65+3.10*x (r=0.74) erent models (CH CCAM TOMCAT TOMCAT TM5 with that of CH PCTM IMPACT ACCESS TM5 (10 mb) [ppb]; 5 ff IMPACT_1x1.25 PCTM 4 TOMCAT Obs TM5_1x1 6 GEOS-Chem_DOH TOMCAT PCTM TM5_1x1 TOMCAT PCTM PCTM Obs IH gradient [ppt] CCAM ACTM CCAM OBS 6 ACTM ACCESS ACCESS OBS GEOS_DOH GEOS_DOH GEOS-Chem GEOS-Chem TM5 erent OH fields. MOZART MOZART 18820 18819 ACTM ACTM CAM CAM IMPACT SF IMPACT_1x1 IMPACT_1x1 ACTM ff IMPACT IMPACT ACTM destruction rate [Tg-CH 4 IMPACT_1x1.25 IMPACT IMPACT 495 500 505 510 515 520 525 CH LMDZ LMDZ (100 mb) -CH , and the modeled vertical gradients in the lower strato- MOZART 4 MOZART 200 400 ACTM_OH ACTM_OH 2003-2007 CH (a) 2000-2007 TOMCAT 1992-1999 0.24 0.28 0.32 490 NIES-08i NIES-08i Averaging period emissions are not as per the protocol. 1 1 5 3 5 3 6 4 2 0 6 4 2 0 6 0

90 80

0.4 0.2

140 130 120 110 100 4

3 3

] yr [ppb rate Growth CH 4

IH gradient [ppt] gradient IH CCl CH

_CTL IH gradient [ppb] gradient IH _CTL CH ) with growth rate. ACTM -1 b Relationships of global loss rates for di Correlation between IH gradients of SF Fig. 10. lated growth rates atsphere surface (100–10 sites mb layer; from this analysis as those models used di Fig. 9. using average valuesexcluded for here the because the period SF 2003–2007 and for 4 sites as in Fig. 7. ACTM Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

0.08 yr)

3 3

Lifetime [yr] Lifetime CCl CH ± 10 8 6 4 2008 2004 lifetimes for EXTRA (9.96 4 2000 18821 Year (A. D.) 1996 (red). Note that the mean CH 3 CCl 3 0.08 yr) fluxes agree within their interannual variability. A comparison of ± 8 9

1992

11

10

4

_CTL Lifetime [yr] Lifetime _CTL CH lifetimes calculated using Eq. (5) and gridded photochemical destruction rates is Time series of median (symbols) and ranges (broken lines) of atmospheric lifetimes of 3 (black) and CH CCl 4 3 and CTL (9.99 CH Fig. 11. CH shown in Fig. S22 (ACTM only).