The evolution of the global aerosol system in a transient ...

Atmos Chem Phys 6 3059ndash3076 2006wwwatmos-chem-physnet630592006copy Author(s) 2006 This work is licensedunder a Creative Commons License

AtmosphericChemistry

and Physics

The evolution of the global aerosol system in a transient climatesimulation from 1860 to 2100

P Stier1 J Feichter1 E Roeckner1 S Kloster1 and M Esch1

1The Atmosphere in the Earth System Max Planck Institute for Meteorology Hamburg Germany now at Department of Environmental Science and Engineering California Institute of Technology Pasadena USA now at Institute for Environment and Sustainability European Commission Joint Research Centre Ispra Italy

Received 4 November 2005 ndash Published in Atmos Chem Phys Discuss 14 December 2005Revised 29 May 2006 ndash Accepted 13 July 2006 ndash Published 24 July 2006

Abstract The evolution of the global aerosol system from1860 to 2100 is investigated through a transient atmosphere-ocean General Circulation Model climate simulation withinteractively coupled atmospheric aerosol and oceanic bio-geochemistry modules The microphysical aerosol moduleHAM incorporates the major global aerosol cycles with prog-nostic treatment of their composition size distribution andmixing state Based on an SRES A1B emission scenario theglobal mean sulfate burden is projected to peak in 2020 whileblack carbon and particulate organic matter show a laggedpeak around 2070 From present day to future conditionsthe anthropogenic aerosol burden shifts generally from thenorthern high-latitudes to the developing low-latitude sourceregions with impacts on regional climate Atmosphericresidence- and aging-times show significant alterations un-der varying climatic and pollution conditions Concurrentlythe aerosol mixing state changes with an increasing aerosolmass fraction residing in the internally mixed accumulationmode The associated increase in black carbon causes a morethan threefold increase of its co-single scattering albedo from1860 to 2100 Mid-visible aerosol optical depth increasesfrom pre-industrial times predominantly from the aerosolfine fraction peaks at 026 around the sulfate peak in 2020and maintains a high level thereafter due to the continu-ing increase in carbonaceous aerosols The global mean an-thropogenic top of the atmosphere clear-sky short-wave di-rect aerosol radiative perturbation intensifies tominus11 W mminus2

around 2020 and weakens after 2050 tominus06 W mminus2 owingto an increase in atmospheric absorption The demonstratedmodifications in the aerosol residence- and aging-times themicrophysical state and radiative properties challenge sim-plistic approaches to estimate the aerosol radiative effectsfrom emission projections

Correspondence toP Stier(philipstiercaltechedu)

1 Introduction

The importance of atmospheric aerosols for the earth systemhas become well established Aerosol particles influence theglobal radiation budget directly by scattering and absorption(Angstrom 1962 McCormic and Ludwig 1967) as well asindirectly by the modification of cloud properties (Twomey1974 Graszligl 1975 Twomey 1977 Albrecht 1989 Hansenet al 1997 Lohmann 2002) with feedbacks to the hydro-logical cycle (Roeckner et al 1999 Liepert et al 2004) Inaddition aerosols link the biogeochemical cycles of the at-mosphere the ocean and the land surfaces acting as micro-nutrients for the marine (Martin and Fitzwater 1988 John-son et al 1997) and terrestrial (Swap et al 1992 Okin et al2004) biosphere However aerosol deposition can also havedetrimental environmental effects such as acidification withimpacts on aquatic and terrestrial ecosystems (egLikensand Bohrmann 1974)

Assessments of the role of aerosols in the earth system andin particular of their climatic impact require the knowledgeof the state of the global aerosol system for past and presentconditions as well as for future scenarios

However while observations provide a wide range of in-formation about the present day global aerosol system theyare not sufficient for an assessment of the aerosol climaticeffects Direct observations of the global aerosol system pro-vide detailed insights into the aerosol system but are rep-resentative of limited spatial and temporal scales Remotesensing data from ground-based lidar and sun-photometersprovides valuable information but suffers from similar sam-pling issues Up to now operational remote sensing datafrom space only provides integral aerosol properties and theretrievals rely on a-priori information about the aerosol sys-tem and internal aerosol models While present-day satelliteobservations allow estimates of the total aerosol radiative ef-fects over the oceans (egZhang et al 2005) fundamentalassumptions have to be made to estimate the anthropogenic

Published by Copernicus GmbH on behalf of the European Geosciences Union

3060 P Stier et al Transient evolution of the global aerosol system from 1860 to 2100

contribution to the radiative effects As natural aerosols aredominated by primary particles in the larger size fraction thefine fraction is typically used as proxy for the anthropogeniccontribution to the aerosol radiative perturbation (Christo-pher and Zhang 2004)

Global aerosol models can contribute to increase the un-derstanding about the complex global aerosol system forpast present and future conditions Furthermore they per-mit to identify the effects of specific aerosol components andaerosol sources natural or anthropogenic on the global cli-mate system

In early transient coupled atmosphere-ocean global cir-culation model (AOGCM) climate simulations the radia-tive impact of anthropogenic aerosols has been neglectedLater modified surface albedos as proxy of the radiative ef-fects of sulfate aerosols have been included (Mitchell et al1995 Meehl et al 1996) The consideration of prognos-tic sulfur cycle schemes in coupled AOGCM climate sim-ulations remains the exception (Roeckner et al 1999 Tettet al 2002 Johns et al 2003) While other anthropogenicaerosol components in particular of carbonaceous aerosolshave received considerable attention (egPenner et al 2001Menon et al 2002 Jacobson 2002) only recently they arebecoming included in transient coupled AOGCM simula-tions (egHansen et al 2005 Takemura et al 2005) Therelative importance of carbonaceous aerosols is projected toincrease according to recent emission scenarios (eg SRESNakicenovic et al 2000) While according to the widelyused SRES A1B scenario anthropogenic SO2 emissions asthe main sulfate precursor are projected to peak in the year2020 the emissions of the carbonaceous aerosols are ex-pected to increase up to the year 2100 Therefore the limi-tation to sulfate aerosols in most previous transient AOGCMclimate simulations is likely to draw an incomplete picturein particular with respect to future climate projections

Here we present results from a transient climate simulationfrom 1860 to 2100 with an evolving earth system model con-sisting of interactively coupled atmosphere and ocean GCMswith embedded atmospheric aerosol cycles and ocean bio-geochemistry In addition to the traditional physical couplingof the atmosphere and ocean models also the atmosphericand oceanic biogeochemical cycles are coupled interactivelyThe microphysical aerosol module HAM (Stier et al 2005)has a prognostic representation of the major global aerosolcomponents with prognostic treatment of their microphysi-cal state and interactions The aerosol components consid-ered are sulfate (SU) black carbon (BC) particulate organicmatter (POM) sea salt (SS) and mineral dust (DU) Aerosolcomposition size distribution and mixing state are prognos-tic Natural emissions of mineral dust sea salt and dimethyl-sulfide (DMS) are calculated interactively Future green-house gas concentrations as well as anthropogenic aerosoland aerosol-precursor emissions are prescribed based on theSRES A1B scenario

The focus of this study is to analyse the transient evolutionof the simulated global aerosol system from 1860 to 2100Other aspects of the results such as the regional climate im-pact of carbonaceous aerosols (Roeckner et al 2006 hence-forth R2006) or the interactive DMS cycle (Kloster et al1henceforth K2006) are addressed in accompanying publi-cations

Section2 describes the setup of the earth system modelThe analysis of the simulated aerosol evolution from 1860 to2100 is presented in Sect3 Section4 concludes the discus-sion

2 Model description

In this study we use a sub-model of the emerging MaxPlanck Institute ndash Earth System Model (MPI-ESM) consist-ing of the following interactively coupled models the atmo-spheric general circulation model ECHAM5 (Roeckner et al2003) the ocean general circulation model MPI-OM (Mars-land et al 2003) the atmospheric aerosol module HAM(Stier et al 2005) and the ocean biogeochemistry moduleHAMOCC5 (Maier 2005) The atmospheric and oceanicbiogeochemical cycles are coupled by accounting for de-position of mineral dust and subsequent iron dissolution asmicro-nutrient for the ocean biogeochemistry and by emit-ting DMS produced by phytoplankton from the ocean sur-face to the atmosphere A detailed description and evaluationof the coupled ECHAM5 ndash HAM system is given inStieret al(2005) and the coupled ECHAM5 ndash HAM ndash MPI-OMndash HAMOCC5 system is evaluated inKloster et al(2006)

21 The atmosphere GCM ECHAM5

The atmospheric GCM ECHAM5 (Roeckner et al 2003)is the fifth-generation climate model developed at the MaxPlanck Institute for Meteorology ECHAM5 solves prognos-tic equations for vorticity divergence surface pressure andtemperature expressed in terms of spherical harmonics witha triangular truncation Non-linear processes and the phys-ical parameterisations are solved on a corresponding Gaus-sian grid Water vapour cloud liquid water cloud ice andtrace components are transported in grid-point space with aflux form semi-Lagrangian transport scheme (Lin and Rood1996) ECHAM5 contains a microphysical cloud scheme(Lohmann and Roeckner 1996) with prognostic equationsfor cloud liquid water and ice Cloud cover is predictedwith a prognostic-statistical scheme solving equations forthe distribution moments of total water (Tompkins 2002)Convective clouds and convective transport are based on themass-flux scheme ofTiedtke (1989) with modifications by

1Kloster S Feichter J Maier-Raimer E Roeckner E Wet-zel P Stier P Six K D and Esch M Response of DMS in theocean and atmosphere to global warming Max Planck Institute forMeteorology Hamburg Germany submitted 2006

Atmos Chem Phys 6 3059ndash3076 2006 wwwatmos-chem-physnet630592006

P Stier et al Transient evolution of the global aerosol system from 1860 to 2100 3061

Table 1 The modal structure of HAM Ni denotes the aerosol number of the modei and Mji

denotes the mass of compoundjisinSU BC POM SS DU in modei The ranges for the number median radiusr give the respective mode boundaries (cfStier et al2005)

Modes SolubleMixed Insolubler [microm]

Nucleationrle0005 N1 MSU

1Aitken0005ltrle005 N2 MSU

2 MBC2 MPOM

2 N5 MBC5 MPOM

5Accumulation005ltrle05 N3 MSU

3 MBC3 MPOM

3 MSS3 MDU

3 N6 MDU6

Coarse05ltr N4 MSU

4 MBC4 MPOM

4 MSS4 MDU

4 N7 MDU7

Nordeng(1994) The solar radiation scheme (Fouquart andBonnel 1980) has 4 spectral bands 1 for the visible andultra-violet and 3 for the near-infrared The long-wave ra-diation scheme (Mlawer et al 1997 Morcrette et al 1998)has 16 spectral bands In this setup surface properties andvegetation cover thus the surface albedo of snow free landsurfaces are fixed at their present day values The surfacealbedo is therefore a function of the snow and ice cover andof their albedo parameterised in terms of the surface tem-perature A resolution of horizontally T63 (corresponding to18) with 19 vertical levels has been applied in the coupledmodel setup

22 The aerosol module HAM

The microphysical aerosol module HAM (Stier et al 2005)predicts the evolution of an ensemble of seven interactinginternally- and externally-mixed log-normal aerosol modesIn the current setup the components sulfate black carbonparticulate organic matter sea salt and mineral dust are in-cluded The aerosol mixing state is prognosed within the pos-sible mixing state configurations illustrated in Table1 Themodes are composed either of compounds with no or low sol-ubility henceforth denoted as insoluble mode or by an inter-nal mixture of insoluble and soluble compounds henceforthdenoted as soluble mode The main components of HAM arethe microphysical core M7 (Vignati et al 2004) an emissionmodule a sulfur chemistry scheme (Feichter et al 1996) adeposition module and a module to calculate the aerosol ra-diative properties

The microphysical core M7 calculates the coagulationamong the modes the condensation of gas-phase sulfuricacid on the aerosol surface the binary nucleation of sulfateand the water uptake Sulfate is assumed to occur semi-neutralised in form of ammonium bisulfate (Adams et al1999)

Aerosol radiative properties as well as the sink processes

dry deposition sedimentation and wet deposition are param-eterised in dependence on the prognostic aerosol size dis-tribution composition and mixing state and coupled to theECHAM5 meteorology The aerosol radiative properties arecalculated in the framework of Mie theory For each aerosolmode effective refractive indices are calculated by volumeaveraging the refractive indices of all components includingaerosol water that is parameterised in dependence of ambi-ent relative humidity The effective complex refractive in-dices and the Mie size-parameters for each mode serve asinput to look-up tables for the aerosol radiative propertiesproviding extinction cross-section single-scattering albedoand asymmetry parameter to the ECHAM5 radiation schemeThe aerosol wet deposition is parameterised in terms of theaerosol size distribution and mixing state via mode specificscavenging ratios specifying in-cloud and interstitial aerosolfractions in the cloudy part of a grid box and in convectiveupdrafts The actual wet deposition is calculated from the re-sulting in-cloud aerosol content based on the precipitationformation and re-evaporation calculated by the ECHAM5cloud scheme

Emissions of mineral dust are calculated online in depen-dence of the ECHAM5 10 m wind speed soil moisture andsnow cover (Tegen et al 2002 2004) Preferential source ar-eas and the vegetation cover are held at their present day val-ues fromTegen et al(2002) Freshly emitted dust is assumedinsoluble Sea salt emissions are parameterised in terms ofthe simulated 10 m wind speed and sea-ice cover followingSchulz et al(2004) Emissions of DMS are calculated inter-actively from the simulated DMS seawater concentrations ofthe HAMOCC5 ocean biogeochemistry applying the sea-airexchange formulation ofWannikhof(1992) that depends onthe simulated 10 m wind speed and sea surface temperature

The applied scenario of prescribed aerosol and aerosol pre-cursor emissions is described in Sect26

wwwatmos-chem-physnet630592006 Atmos Chem Phys 6 3059ndash3076 2006


23 Aerosol-cloud coupling

The standard ECHAM5 cloud scheme has been extendedby a prognostic equation for the cloud droplet number con-centration (CDNC) (Lohmann et al 1999) Nucleation ofcloud droplets is parameterised semi-empirically in termsof the aerosol number size distribution and vertical veloc-ity (Lin and Leaitch 1997) Sub-grid scale vertical velocityis derived from the turbulent kinetic energy (Lohmann andKarcher 2002) CDNC sink processes are parameterised inanalogy to those formulated in ECHAM5 for the in-cloudliquid water content The cloud radiative properties dependon the droplet effective radius which is calculated from theliquid water content and CDNC These co-determine alsothe auto-conversion rate followingKhairoutdinov and Kogan(2000) Thus this setup accounts for both the first and sec-ond indirect aerosol effects The semi-direct aerosol effectsdefined here as the first order response of the cloud system tothe direct aerosol radiative effects via the associated changesof the temperature and humidity structure of the atmosphereand of the surface fluxes are implicitly included by the cou-pling of the aerosols and the ECHAM5 radiation scheme

24 The ocean GCM MPI-OM

The ocean GCM MPI-OM (Marsland et al 2003) is basedon the primitive equations for a hydrostatic Boussinesq fluidwith a free surface The bottom topography is resolved bymeans of partial grid cells The poles of the curvilineargrid are shifted to land areas over Greenland and AntarcticaParameterised processes include along-isopycnal diffusionhorizontal tracer mixing by advection with unresolved ed-dies vertical eddy mixing near-surface wind stirring con-vective overturning and slope convection Concentrationand thickness of sea ice are calculated by means of a dynamicand thermodynamic sea ice model In the coupled AOGCMsetup (Jungclaus et al 2006) the ocean passes to the atmo-sphere the sea surface temperature sea ice concentration seaice thickness snow depth on ice and the ocean surface ve-locities Using these boundary values the atmosphere modelaccumulates the forcing fluxes during the coupling time stepof one day The daily mean fluxes are then passed to theocean A horizontal resolution of 15 and a vertical discreti-sation on 40 Z-levels is applied in the coupled model setupNo flux adjustment is employed

25 The ocean biogeochemistry module HAMOCC5

The Hamburg oceanic carbon cycle model (Maier 2005) iscoupled online to the circulation and diffusion of the MPI-OM The embedded ecosystem model is based on nutri-ents phytoplankton zooplankton and detritus (NPZD-type)as described bySix and Maier-Reimer(1996) In addi-tion new elements such as nitrogen dissolved iron anddust are accounted for and new processes like denitrifica-

tion and nitrogen-fixation formation of calcium carbonateand opaline shells DMS production and consumption pro-cesses dissolved iron uptake and release by biogenic parti-cles as well as dust deposition and sinking are implemented(MaierndashReimer et al 2005) Iron is released into the oceansurface from the dust deposition flux of the HAM aerosolmodule assuming a dust iron content of 35 The DMSproduction is parameterised in terms of the degradation ofphytoplankton by senescence and grazing processes DMSsink processes are consumption by bacteria chemical oxida-tion and the flux to the atmosphere passed as emission fluxto the aerosol module HAM

26 Simulation setup

The experimental design follows the standard procedure incoupled atmosphere-ocean climate simulations From a pre-industrial control experiment ldquo20th centuryrdquo integrations arestarted and complemented from the year 2000 on by a futurescenario simulation based on SRES A1B Two 20th centuryensemble realisations have been performed Here we presentresults for 1860 to 2000 from the first realisation togetherwith the 2001 to 2100 A1B scenario run that was initialisedfrom the second realisation

In the control simulation the concentrations of well-mixed greenhouse gases are fixed at their 1860 val-ues (CO2=2862 ppmv CH4=8056 ppbv N2O=2767 pptvCFC-11=125 pptv CFC-12=0 where CFC-11 accountsfor the radiative effect of minor species including a smallcontribution from natural sources) and ozone is prescribed asin ECHAM5 (Roeckner et al 2003)

The 20th century run is initialised from a balanced stateof the control run Well-mixed greenhouse gases are pre-scribed annually according to observations (smoothly fittedto ice core data direct observations and SRES values for theyear 2000) Monthly stratospheric and tropospheric ozoneconcentrations are prescribed as two-dimensional (latitudeheight) distributions (Kiehl et al 1999) From pre-industrialto present day conditions optical depths of stratosphericaerosols from volcanic eruptions are prescribed annually infour latitude bands based on an updated dataset (httpwwwgissnasagovdatastrataer) of Sato et al(1993) Variationsin solar irradiance are specified according toSolanki andKrivova (2003)

We periodically apply monthly mean year 2000 offline ox-idant fields (OH H2O2 NO2 O3) for the sulfur chemistryscheme as used inStier et al(2005) This simplificationcan be justified by the results ofPham et al(2005) who in-vestigated the effect of changes in the oxidation fields from2000 to 2100 on the global sulfate distribution based on theSRES A2 scenario They showed that the effect on the globalmean sulfate burden is less than 1 and on regional surfaceconcentrations about 5 SimilarlyUnger et al(2006) anal-ysed the effect of changes in the oxidation fields from 2000to 2030 on the formation of sulfate Based on the SRES A1B



scenario they find a global mean effect on the sulfate burdenof less than 2 and regional effects on surface mixing rationswith maxima of about 20

We further periodically apply monthly mean emissionsof biogenic terrestrial DMS and POM from secondary bio-genic sources as well as SO2 emissions from continu-ously degassing volcanoes based on the year 2000 Ae-roCom aerosol model inter-comparison experiment (httpnansenipsljussieufrAeroCom) emission inventory (Den-tener et al 2006 available from ftpftpeijrcitpubAerocom) POM from secondary biogenic sources is thereinestimated assuming an aerosol yield of 015 from the bio-genic monoterpene emissions ofGuenther et al(1995) andapplied in HAM as primary aerosol source The emissionsize distributions of BC and POM follow the AeroCom rec-ommendations (seeStier et al 2005) as well as the assump-tion that 25 of all SO2 emissions are emitted in form ofprimary sulfate

Transient emission fluxes from 1860 to 2100 of SO2 andBC from fossil fuel combustion domestic fuel-wood con-sumption agricultural waste burning and forest fires are pre-scribed from a compilation by the Japanese National Institutefor Environmental Studies (NIES T Nozawa et al personalcommunication 2004)

Historic SO2 emissions are based onLefohn et al(1999)complemented by shipping emissions from the HYDEdatabase (httpwwwmnpnlhyde) BC biomass burn-ing emissions are from the GEIA database (httpwwwgeiacenterorg) for the year 1987 employing the methodol-ogy of Cooke et al(1996) Pre-industrial biomass burningemissions are assumed to be 10 of the present day emis-sions (Andreae 1991) and are scaled to present day condi-tions proportionally to the global population increase fromthe HYDE database Present day emissions of BC fromfossil-fuel (Cooke et al 1999) as well as from biofuel andagricultural activities (Takemura et al 2000) are scaled topre-industrial values employing World Bank gross domesticproduct data for each country

Future emissions from 2000 to 2100 are based on theSRES A1B scenario Anthropogenic SO2 emissions are usedas provided by SRES (Nakicenovic et al 2000 availablefrom httpsresciesinorg) For the carbonaceous aerosolsthe present day fossil fuel emissions are scaled according tothe individual source trends in the SRES data BC biomassburning and biofuel emissions are extrapolated from thepresent day emissions based on the SRES A1B populationscenario BC emissions from agricultural activities are ex-trapolated proportionally to the cropland development in theSRES land use data

For the total integration period we derived emissionsof SO2 from vegetation fires and of POM from the BCemissions by assuming source specific emission ratiosSO2BC=128 POMBC of 14 (fossil fuel) 56 (domesticand agricultural) and 11 (vegetation fires) (F Dentener per-sonal communication)

It has to be pointed out that even for present day emis-sion inventories based on largely well determined fuel usedata significant uncertainties exist These uncertainties areparticularly large for the carbonaceous compounds so thatpresent day inventories differ by as much as a factor of twofor fossil fuel use (egSchaap et al 2004) and are even moreuncertain for biomass burning emissions These uncertain-ties propagate into past and future emission scenarios andfurther add to their uncertainties regarding economic pop-ulation technological and legislative developments As aresult available inventories for the evolution of the emis-sions of carbonaceous aerosols differ significantly For ex-ample fossil- and bio-fuel emissions of the used NIES emis-sion compilation for BC (POM) are for pre-industrial condi-tions 1 Tg (3 Tg) and for present-day conditions 9 Tg (25 Tg)thus higher than the estimate ofIto and Penner(2005) withpre-industrial emissions of about 1 Tg (4 Tg) and present-dayemissions of about 5 Tg (13 Tg) The differences are largerfor the more uncertain wildfire emissions of BC (POM) ofpre-industrial 1 Tg (7 Tg) and present-day 8 Tg (83 Tg) in theused NIES emission compilation compared to pre-industrial1 Tg (11 Tg) and present-day 4 Tg (30 Tg) emissions derivedby Ito and Penner(2005) For the future evolution of car-bonaceous aerosols the used NIES SRES A1B emission com-pilation and the underlying original SRES A1B estimate (Na-kicenovic et al 2000) project a significant increase duringthe 21st century However the emission inventory byStreetset al(2004) based on the same SRES A1B storyline but con-sidering estimated impacts of technological advancementsprojects a decrease of the BC (POM) aerosol emissions from1996 to 2030 of 11 (16) and from 1996 to 2050 of 24(18) Rao et al(2005) predict for both the SRES A2 andeven for the environmentally conscious B1 scenario an ini-tial increase of fossil- and bio-fuel BC emissions by about60 (estimated from their Fig 4) from 2000 to about theyear 2035 and a decrease thereafter Their POM emissionsare projected to increase to about 2035 under the A2 sce-nario and are relatively constant until the year 2040 underthe B1 scenario See discussion inStreets et al(2004) Itoand Penner(2005) andRao et al(2005) for more details

The diversity in the emission inventories even for an iden-tical storyline of economic development such as SRES A1Bstress the scenario character of aerosol-climate simulationsIt should be kept in mind that each scenario represents onlyone possible image of how the future might unfold under theassumed external conditions

3 Results

31 Emissions

The global annual total aerosol and aerosol precursor emis-sions from 1860 to 2100 are displayed in Fig1 Emissions ofSO2 dominated by fossil fuel use peak with 120 Tg around



SO2

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

120

140

Em

issi

ons

[Tg(

S)]

TotalFossil FuelsVegetation FiresVolcanoes

DMS

1860 1900 1950 2000 2050 2100Year

20

21

22

23

24

25

26

Em

issi

ons

[Tg(

S)]

σnorm = 001

Black Carbon

1860 1900 1950 2000 2050 2100Year

0

10

20

30

40

Em

issi

ons

[Tg]

TotalFossil FuelsVegetation FiresAgricultural WasteDomestic Fuels

Sea Salt

1860 1900 1950 2000 2050 2100Year

5800

6000

6200

6400

6600

Em

issi

ons

[Tg]

σnorm = 001

Particulate Organic Matter

1860 1900 1950 2000 2050 2100Year

0

50

100

150

200

Em

issi

ons

[Tg]

TotalFossil FuelsVegetation FiresAgricultural WasteDomestic FuelsBiogenic

Dust

1860 1900 1950 2000 2050 2100Year

800

1000

1200

1400

1600E

mis

sion

s [T

g]σnorm = 008

Fig 1 Global annual source specific and total aerosol and precursor emissions from 1860 to 2100 For the natural emissions also thenormalised inter-annual standard deviationσnorm is shown

year 2020 and decrease to 45 Tg in the year 2100 Con-trary dominated by the contribution from vegetation firesthe POM emissions peak with about 190 Tg in the year 2050and decrease to values around 160 Tg in the year 2100 Asthe contribution from fossil fuels is projected to increase upto the year 2080 the BC emissions show a lagged peak reach-ing values of 30 Tg around year 2070 and only a small de-crease to the year 2100

The trends of the interactively calculated natural emissionsare significantly smaller as distinct trends can only be ex-pected as result of significant alterations of the climatologicalstate The inter-annual variability is analysed in terms of thenormalised inter-annual standard deviationσ norm

=σ(Eprime)EwhereEprime is the inter-annual emission flux perturbation cal-culated as difference between the annual-mean emission fluxand its 20-year running mean andE is the integration-periodmean emission flux

Although the global annual mean wind speed increases(not shown) the wind-driven emissions of DMS constantlydecrease in total by about 10 from the years 1860 to2100 This global mean reduction is the consequence of aregionally inhomogeneous reduction of the DMS sea sur-

face concentration attributable to dynamical changes of theocean mixed layer under a warming climate and the result-ing less favourable nutrient and light supply for phytoplank-ton growth and the associated DMS production The evo-lution of the global DMS cycle will be discussed in an ac-companying publication (K2006) The inter-annual variabil-ity is small with σ norm

=001 The sea salt emissions showno significant trend throughout the simulation period and asmall inter-annual variability (σ norm

=001) Mineral dustemissions however show a distinct inter-annual variability(σ norm

=008) and an increase of about 10 towards the endof the integration period As most of the mineral dust emis-sions are confined to small scale preferential source areas(Tegen et al 2002) they are particularly sensitive to changesin the regional meteorology A more detailed analysis (notshown) reveals that the dominant changes in the dust emis-sions occur in the northern African source regions with a de-crease in the central-north African source regions includingthe Bodele Depression and an increase in the Saharan north-western African source regions The decrease in dust emis-sions in central-north Africa can be attributed to an increasein soil moisture and reduced surface windspeeds Contrary



Sulfur 447 Tg (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1019 Tg (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1166 Tg (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20

Black Carbon 17 Tg (1861-1890)

0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10

Organic Carbon 314 Tg (1861-1890)

0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10

Fig 2 Global annual total 30-year average aerosol and precursor emissions for the periods 1861ndash1890 1961ndash1990 and 2021ndash2050Contours in g mminus2 yrminus1

the increased emission in the north-western Saharan sourceregions are a consequence of increased surface wind speedsThese changes of the regional climatological conditions canpartly be attributed to an alteration of the monsoon regimesowing to an increase in atmospheric absorption due to in-creased carbonaceous emissions from vegetation fires (seeR2006) It has to be pointed out that the dust emissionsare calculated assuming fixed preferential source areas andyear 2000 vegetation cover Therefore the simulated centuryscale variability is likely to be a lower estimate

The evolution of the global distribution of the emissionsof sulfur black carbon and particulate organic matter is il-lustrated in Fig2 Shown are the totals and distribution ofthe global annual aerosol and precursor emissions averagedover 30-year periods It is clearly discernible that from the1861ndash1890 to the 1961ndash1990 period the dominant emissionincrease took place at the east coast of the US in Central Eu-rope and also in China Contrary from 1961ndash1990 to 2021ndash2050 the US and European emissions are projected to de-crease and significant enhancements are expected in the lowlatitude regions South America Central and South Africaand South Asia

32 Atmospheric aerosol burdens

The changes in the atmospheric emissions are reflected inthe atmospheric aerosol column burdens shown as totalsand global distribution for sulfate black carbon and par-ticulate organic matter as 30-year averages for the periods1861ndash1890 1961ndash1990 and 2021ndash2050 in Fig3 Resem-bling the emission changes the SU aerosol burden increasesparticularly in the northern hemispheric source regions fromthe 1861ndash1890 to the 1961ndash1990 period With increasingemissions also the export from the sources regions increasesparticularly from Europe to the Mediterranean and northernAfrica To the 2021ndash2050 period the high latitude emissionsare projected to decrease and high values of the burden arelargely confined to low latitude regions For BC and POM thecontribution of the low-latitude emissions increases through-out the integration period With decreasing high-latitude BCand POM emissions from 1961ndash1990 to 2021ndash2050 the low-latitude atmospheric aerosol burden is projected to dominatefor the future conditions

The temporal evolution of the atmospheric aerosol burdenis shown as total and separated for four equal area latitude



Sulfate 04 Tg(S) (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 09 Tg(S) (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 12 Tg(S) (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20

POM 04 Tg (1861-1890)

0 01 02 05 1 2 5 10 20 50 100 200

POM 15 Tg (1961-1990)

0 01 02 05 1 2 5 10 20 50 100 200

POM 35 Tg (2021-2050)

0 01 02 05 1 2 5 10 20 50 100 200

Fig 3 Global total 30-year average aerosol burdens for the periods 1861ndash1890 1961ndash1990 and 2021ndash2050 Contours in mg(S) mminus2 forSU and mg mminus2 for BC and POM

bands in Fig4 As the aerosol residence-time is shortaerosols do not accumulate and the trends in the global an-nual mean aerosol burdens to a first order resemble the trendsin the aerosol emissions with a peak of the SU aerosol burdencentred around the year 2020 maximum values of POM bur-den around 2050 and a maximum of BC around 2070 Mostprominently for BC and POM but to a minor degree also forSU the dominant aerosol increase occurs at low latitudesThis can be attributed to the fact that a significant part of theprojected emission increase is from tropical vegetation firesand from increased fossil fuel usage in developing countriesFor sea salt the total aerosol burden and meridional distribu-tion shows only minor variations The increase in the dustburden discernible from the year 2000 onwards is most pro-nounced in the 0 to 30 N band owing to the northward shiftof the African dust emissions described in the last section

33 Atmospheric residence-times

As many greenhouse gases have long and approximately con-stant atmospheric residence-times the discussion about pol-lutant mitigation is generally expressed in terms of emissionscenarios implicitly assuming that the atmospheric burden is

directly linked to the global total amount of emissions Forthe short lived aerosols however the atmospheric residence-time (τ ) is not necessarily constant and depends on the timeand point of emission chemical thermodynamical and mi-crophysical transformations (ldquoagingrdquo) and on the meteoro-logical conditions along the aerosol trajectories (Graf et al1997 Barth and Church 1999 Stier et al 2006) Fig-ure5 shows the evolution of the component residence-timesthroughout the integration period It is clearly discerniblethat the residence-time shows non-negligible variations forall components Changes in precipitation affect the aerosolresidence time via the interactively calculated wet depositionrate However the general increase of the residence timefrom about 1950 onwards cannot be explained by the changein precipitation which actually increases by about 3 from1860 to 2100 (not shown) The interpretation of this com-plex evolution of the atmospheric residence-times involves anumber of competing processes and interactions

For SUτ decreases from about 45 days at pre-industrialtimes to about 4 days around 1950 Thereafter in particu-lar with the distinct shift to low-latitude source regions af-ter 2025 and high burdens in the arid subtropical regions



Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

σnorm = 002

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

σnorm = 009

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Fig 4 Global total atmospheric aerosol burdens from 1860ndash2100 (bold line) accumulated from bottom to top over the equal area latitudebands from 90 S to 30 S from 30 S to 0 from 0 to 30 N and from 30 N to 90 N (hatched)

(Figs2 3 4) τ increases reaching about 55 days around2050 and remains relatively stable afterwards

For BC τ decreases from 65 days in 1860 to around 5days in 1960 and increases thereafter to to 8 days in 2100The initial decrease in the residence-time is contradictory tothe increasing importance of low latitude dry-season vege-tation fire emissions (cf Figs2 3) However for the ini-tially emitted insoluble BC microphysical aging processesplay an important role From Fig6 depicting the evolu-tion of the microphysical aging-time ie the timescale oftransformation from the insoluble to the soluble modes (seeStier et al 2005) it becomes evident that the BC aging-timeis approximately halved from 1860 to 1950 As a conse-quence the mass fraction of BC residing in the efficiently

scavenged internally-mixed accumulation mode soluble in-creases (Fig7) explaining the initial decrease in the BCresidence-time From about 1960 onwards the increase inthe residence-time indicates that the further enhanced agingto the peak of the sulfate burden in 2020 is outweighed bythe shift to low latitude emissions with a large contributionof dry-season vegetation fire emissions

For POM after a slow increase from about 45 days in1860 to 55 days around 1960τ increases more rapidly to85 days in the year 2100 closely tracking the evolution forBC This is a result of the increasing relative importance ofthe low-latitude dry-season vegetation fire emissions fromwhich BC and POM are co-emitted



Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]

SulfateBlack CarbonParticulate Organic MatterDustSea Salt

Fig 5 Global mean atmospheric aerosol component residence timefrom 1860ndash2100

Microphysical Aging Time

1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day

s]Black CarbonParticulate Organic MatterDust

Fig 6 Global mean atmospheric microphysical component aging-time from 1860ndash2100

Interestingly the residence-time of SS also increases byabout 6 from 1860 to 2100 Changes in the surface windswith a poleward shift of the mid-latitude tropospheric wester-lies (see R2006) cause a poleward shift of the SS emissionsThis shift is particularly pronounced in the southern latitudesAssociated is a small but continuous shift of the sinks fromwet deposition to turbulent dry deposition and sedimentation(not shown) As the high latitude regions are dominated byice clouds with a slightly reduced scavenging efficiency (cfStier et al 2005) this could explain the increase in the SSresidence-time

For DUτ increases from 2000 to 2100 continuously fromabout 5 to 6 days This is caused by the north-western shiftof the dominant African sources into more arid regions andsupported by an associated shift of the sinks from wet de-position to sedimentation The enhanced microphysical ag-ing under more polluted conditions indicated by decreasedaging-times (Fig6) and the associated enhanced mass frac-tion in the soluble coarse mode (Fig7) that potentially re-ducesτ appears to be a second order effect Nonetheless itcould explain the relative stable residence-times from 1860to 2000 and the enhanced increase after the peak of the sul-fate burden in 2020

34 Aerosol mixing state

A number of aerosol parameters such as the mixing state arefixedly imposed in the mass based bulk modelling approachapplied in traditional global aerosol models However theobserved large internally-mixed aerosol population is a clearindicator that the mixing state is not constant for differentlevels of emissions and therefore not under different climaticregimes The application of the microphysical aerosol mod-ule in a transient climate simulation allows to investigate theevolution of previously imposed parameters from prognosticvariables

The evolution of the global mean aerosol component masspartitioning among the seven aerosol modes of HAM (Ta-ble 1) from 1860ndash2100 is shown in Fig7 For the anthro-pogenically relevant species SU BC and POM it is evidentthat under the higher polluted conditions their mass shiftsfrom the Aitken modes to the internally mixed accumulationmode soluble This mode is of particular importance for theaerosol radiative effects On the one hand hydrophilic parti-cles in that size-range serve as cloud condensation nuclei andplay therefore a key role for the indirect aerosol effects Onthe other hand it follows from Mie theory that particles inthis size-range have the highest extinction efficiency for thevisible wavelengths and therefore the strongest potential tocontribute to the direct aerosol effects For DU a larger massfraction is aged to the soluble modes under more pollutedconditions consistent with the evolution of the microphysi-cal aging-time shown in Fig6 The soluble mass fraction ofDU decreases with the decay of the SU and POM burdens

The disproportionate emission changes of the differentaerosol components and precursors (Sect31) imply alter-ations of the composition of internally mixed modes Thesimulated evolution of the composition of the internallymixed modes of HAM is displayed in Fig8 Sulfate iswith a relatively constant mass-fraction of 90 the domi-nant component of the soluble Aitken mode from 1860 toaround 2020 Thereafter the sulfate fraction decreases to80 in 2100 as the carbonaceous contribution increases Forthe Aitken mode insoluble the mass fraction of POM grad-ually decreases from 90 in 1860 to 70 in 2100 balancedby an increase in BC This is largely a consequence of the in-creasing emission ratio of BC to POM For the coarse modesoluble the mass fraction of DU increases from about 35to 45 balanced by a decrease in sea salt attributable to en-hanced microphysical aging of dust and a relative increase inthe total DU burden

The evolution of the radiatively important internally-mixed accumulation mode soluble is more complex Withthe increase in the anthropogenic emissions the contribu-tions of the natural components DU and SS are reducedfrom about 35 in 1860 to 10 in the year 2020 The in-crease in the sulfate mass is outweighed by the increase incarbonaceous aerosols particularly POM so that the massfraction of sulfate decreases constantly This decrease is



Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

Nucleation SolubleAitken SolubleAccumulation SolubleCoarse SolubleAitken InsolubleAccumulation InsolubleCoarse Insoluble

Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Fig 7 Global mean aerosol component partitioning among the seven aerosol modes of HAM from 1860ndash2100

enhanced after the sulfate peak in the year 2020 so that POMbecomes the dominant component in the accumulation modesoluble after 2050 The mass fraction of BC increases con-stantly from 0 in 1860 to around 7 in 2100

35 Aerosol radiative properties and perturbations

These changes in the composition of the internally mixedmodes with a relative increase in the carbonaceous aerosolshave distinct effects on their interactively calculated radia-tive properties Here we focus on the internally-mixed ac-cumulation mode soluble as it dominates the anthropogeniccontribution to the aerosol radiative effects The effect ofthe increasing importance of the carbonaceous aerosols isnicely demonstrated by the evolution of the global mean op-tical depth weighted co-single scattering albedo (CO-SSA)at a wavelength of 550 nm depicted in Fig9 The CO-SSAas a measure of the contribution of absorption to the total ex-tinction increases constantly from 002 in 1860 to 004 in2020 This is a consequence of the increase in the carbona-ceous mass fraction particularly of BC with higher imag-inary parts of the refractive indices (seeStier et al 2005)With the decline of the projected sulfate emissions in 2020the increase in the CO-SSA is further enhanced reaching aplateau of more than 007 in the year 2070

In summary the projected changes in the aerosol emis-sions distinctively affect the aerosol mixing state and the

composition of the internally mixed modes on the globalscale Consequently their radiative properties are alteredwith a significant enhancement of the absorption efficiencyowing to the increased contribution of carbonaceous aerosolsthroughout the integration period

Changes in the atmospheric aerosol burden and composi-tion affect the aerosol optical depth (AOD) ie the columnintegrated aerosol extinction The evolution of the globalmean total tropospheric aerosol optical depth fine mode op-tical depth (Aitken and accumulation modes) coarse modeoptical depth and the absorption optical depth at 550 nm isshown in Fig11 Total AOD increases from a pre-industriallevel of 015 to 026 in year 2020 The decrease in the SUcontribution after 2020 is partly compensated by the increasein POM so that AOD decreases only weakly and levels off to023 in the year 2100 It is interesting to note that the domi-nant increase in total AOD can be attributed to an increase inthe fine mode AOD confirming the assumption that anthro-pogenic aerosols predominantly affect the fine mode aerosoloptical depth However these results do not support the re-verse ie that the fine mode optical depth is a direct measureof the anthropogenic aerosol radiative effects In fact morethan a third of the simulated fine mode optical depth for theyear 2000 is of natural origin indicated by the values at thebeginning of the integration period The natural fine modeoptical depth is dominated by volcanic sulfate but shows also



contributions from DMS derived sulfate biogenic POM aswell as from sub-micron sea salt and mineral dust

In Fig 10 the simulated 1998-2002 mean aerosol opticaldepth is compared on a regional basis to five different mea-surement datasets to a retrieval from the AERONET sun-photometer network (Holben et al 2001) extrapolated andgridded on a 1times1 resolution (1998ndash2004 mean of availablemeasurements S Kinne personal communication 2006) aswell as to satellite retrievals from MODIS (2000-2004 meanTanre et al 1997 Kaufman et al 1997) MISR (2000ndash2004 meanMartonchik et al 2002 2004) AVHRR (2001meanIgnatov and Stowe 2002ab) and TOMS (1996ndash2000meanTorres et al 2002) For comparison results fromthe Stier et al(2005) year 2000 reference simulation withECHAM5ndashHAM are provided utilising the AeroCom emis-sion inventory At large the regional distribution of the ob-served AOD is well captured in the MPIndashESM simulationThe global 1998ndash2002 mean AOD of 022 is somewhat largerthan the global 2000ndash2004 mean AODs from MODIS (020)and MISR (021) that provide probably the highest qualitydatasets with global coverage However this good agreementon a global mean basis should be regarded with the caveatthat the satellite retrievals exhibit non-negligible uncertain-ties and both the MODIS and MISR retrievals are likely to bebiased high (Levy et al 2005 Liu et al 2004) On a regionalbasis the overestimation of AODs is particularly pronouncedin the Saharan dust outflow region but also in the main an-thropogenic source regions China Europe and the easternUnited States It is not possible to quantitatively attribute thisoverestimation to specific causes but the comparison to theECHAM5ndashHAM reference simulation (global annual meanAOD of 014) can give further insight Higher emissions ofmineral dust of about 1200 Tg yrminus1 around year 2000 in thefree climate mode compared to emissions of about about 670Tg yrminus1 in the nudging mode applied in the reference simula-tion contribute to the overestimation of AOD in the Saharanoutflow region These higher emissions are a consequenceof a shift of the surface wind speed frequency distributionto higher wind speeds in the climate mode SeeTimmreckand Schulz(2004) for more details Stronger anthropogenicemissions in the NIES emission inventory compared to theAeroCom inventory contribute to the overestimation of theaerosol optical depths close to the main anthropogenic sourceregions compared to both the remote sensing data and the ref-erence simulation

The evolution of the absorption aerosol optical depth(AAOD) ie the column integrated aerosol extinction ow-ing to absorption (Fig11) shows a distinct increase frompre-industrial levels of around 0001 and levels off around001 in the year 2070 This increase is dominated by the in-crease in the fine mode AAOD attributable to the increase inthe total BC burden (linear Pearsonrsquos correlation coefficientr=0999) The small increase in coarse mode AAOD can beattributed to the increase in the DU burden (r=0995)

The development of the aerosol distribution and radiativeproperties determines their direct effects on the global radi-ation balance Figure12 shows the evolution of the simu-lated global mean total aerosol short-wave clear-sky directradiative perturbation (DARP) together with the prescribedaerosol optical depth owing to volcanic aerosols in the strato-sphere DARP is defined here as the deviation of the clear-sky net short wave radiation at the top of the atmosphere fromthe 1860ndash1870 mean DARP is calculated as the change inthe clear-sky net top-of-the-atmosphere solar radiation cor-rected for variations in the top-of-the-atmosphere solar irra-diance and for the associated change in the upward surfaceradiation The correction for the change in the upward sur-face radiation from the stored model variables requires theassumption of constant surface albedos Therefore areaswith a change in surface albedo larger than 003 as well assea-ice covered regions are masked out The masked areasconstitute generally less than 14 of the earthrsquos surface

Superimposed to the anthropogenic trends are distinctDARPs from volcanic eruptions reaching values of aroundminus4 Wmminus2 for the major Krakatoa (1883) and MountPinatubo (1991) eruptions With increasing anthropogenicAOD the negative DARP intensifies reachesminus08 Wmminus2

around 2000 peaks with aboutminus11 Wmminus2 around 2020 andremains relatively stable up to 2050 largely because the con-tinued increase in POM outweighs the decrease in SU after2020 Although AOD remains at a higher level than in 2000thereafter DARP weakens reachingminus06 Wmminus2 in 2100This can be attributed to an increase in atmospheric absorp-tion owing to the BC increase (Fig11) It is interestingto note that the combination of volcanic and anthropogenicaerosol perturbations between about 1950 and 1970 causesa distinct negative DARP of up tominus2 Wmminus2 This negativeradiative perturbation contributes to mask out the effect ofincreased greenhouse gas emissions on the global tempera-ture In combination with the stagnation and even reversalof the increase of the solar irradiance after about 1930ndash1940(Solanki and Krivova 2003 Krivova and Solanki 2004) thisexplains the well simulated small trend in global surface tem-peratures between 1950 and 1970 (see Fig 1 in R2006) de-spite the increasing positive greenhouse gas forcing (see alsodiscussion inMcAvaney et al 2001)

4 Conclusions

The evolution of the global aerosol system from 1860 to 2100is investigated through a transient atmosphere-ocean GCMclimate simulation with interactively coupled atmosphericaerosol and oceanic biogeochemistry modules The micro-physical aerosol module HAM incorporates the major globalaerosol components sulfate black carbon particulate organicmatter sea salt and mineral dust with prognostic treatmentof their composition size distribution and mixing state



Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]

SulfateBlack CarbonParticulate Organic MatterSea SaltDust

Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Accumulation Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Fig 8 Global mean aerosol composition of the internally mixed aerosol modes of HAM from 1860ndash2100

The atmosphere and ocean GCMs are coupled interac-tively employing no flux correction In addition also theatmospheric and oceanic biogeochemical cycles are coupledinteractively by accounting for the deposition of mineral dustacting as micro-nutrient for a prognostic ocean biogeochem-istry scheme and by emitting biogeochemically producedDMS from the ocean surface to the atmosphere Also thenatural emissions of mineral dust and sea salt are calculatedinteractively Anthropogenic aerosol and precursor emis-sions are prescribed based on the Japanese National Institutefor Environmental Studies emission inventory from 1860 to2100 From pre-industrial to present day times greenhousegases volcanic stratospheric AOD and solar variability areprescribed according to observations For the 2000 to 2100period greenhouse gas concentrations as well as aerosol andprecursor emissions are based on the SRES A1B scenario

From pre-industrial times to 2020 the global mean sul-fate aerosol burden is projected to increase from 04 Tg(S)to 13 Tg(S) and thereafter to decrease to 08 Tg(S) in 2100Aerosol burdens of BC and POM are increasing up to around2070 peaking with burdens of 07 Tg and 4 Tg and show asmall decrease thereafter The burdens of natural sea salt andmineral dust also increase however at a significantly slowerrate It has to be pointed out that the dust emissions are cal-culated assuming fixed preferential source areas and vegeta-tion cover Natural secondary organics are prescribed at theiryear 2000 levels Thus the simulated variability and trends

CO-SSA - Accumulation Soluble

1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]

Fig 9 Global mean optical depth weighted accumulation modesoluble co-single scattering albedo at 550 nm from 1860ndash2100

of mineral dust and natural secondary organics potentiallyrepresent a lower estimate owing to the neglect of land-useand vegetation changes

Regionally the prognosed emissions and consequently thesimulated aerosol burden show inhomogeneous trends Frompresent day to future conditions the anthropogenic aerosolburden shifts generally from the northern high-latitudes tothe developing low-latitude source regions The resultingspatially inhomogeneous radiative perturbations are a driv-ing force for regional climate change



MPI-ESMAERONETMODISMISRTOMSAVHRRECHAM5-HAM

AOD (550nm) 01 02 03 04

Fig 10 Regional comparison of simulated aerosol optical depth at 550 nm (red 1998ndash2002 mean) to retrievals from the AERONET sun-photometer (yellow 1998ndash2004 mean of available measurements) and from the satellite instruments MODIS (dark blue 2000ndash2004 mean)MISR (light blue 2000ndash2004 mean) TOMS (dark green 1996ndash2000 mean) and AVHRR (light green 2001 mean) Grey shading indicatesthe averaging areas For comparison results from theStier et al(2005) year 2000 reference simulation with ECHAM5-HAM employingthe AeroCom emission inventory are given (orange)

The projected increase in low-latitude carbonaceousaerosols and the associated increase in the atmospheric ab-sorption cause an enhancement of local monsoon regimesparticularly pronounced over Central Africa (see R2006)The associated changes in the flow pattern and the increasein precipitation and soil moisture shifts emission regimes andthe atmospheric burden of mineral dust northward Such cou-plings of the global aerosol cycles acting in addition to thecoupling by microphysical processes (Stier et al 2006) willbe further enhanced when climate-vegetation feedbacks aretaken into account

An analysis of atmospheric residence-times reveals signif-icant alterations under varying climatic and pollution con-dition during the integration period Thus the atmosphericaerosol burden and therefore the aerosol radiative effects can-not be scaled by global annual mean emission data The evo-lution of the aerosol burden is rather the result of complex in-teractions of aerosol microphysics formation pathways thepoint of emission and the meteorological conditions alongthe aerosol trajectories For example a given sulfate radia-tive perturbation in the year 2000 scaled to the year 2100 bythe change of SO2 emissions would be biased low by a fac-tor of τ2000τ2100=43 days56 days= 08 solely due to theneglect of the longer residence-time in 2100 (assuming con-stant sulfate yield from SO2 emissions and constant aerosolradiative properties)

In previous climate simulations the microphysical aging-time of BC POM and DU if considered has been pre-scribed as constant Here we show that it varies by as muchas a factor of two during the integration with enhanced agingunder polluted conditions peaking around the year 2020

The projected inhomogeneous changes in the aerosol andprecursor emissions distinctively affect the aerosol mixingstate and the composition of the internally mixed modesWith increasing levels of anthropogenic pollution the frac-tion of SU BC and POM residing in the radiatively impor-tant internally mixed accumulation mode increases owingto enhanced microphysical interactions Under the predictedemission changes the global mean composition of the inter-nally mixed accumulation mode is altered with a steady in-crease in the contribution of carbonaceous aerosols Thesecomposition changes are reflected in the aerosol radiativeproperties The increasing fraction of carbonaceous aerosolsin the internally-mixed accumulation mode causes a morethan threefold increase in its co-single scattering albedo from1860 to 2100 These findings are in contradiction to the tra-ditional approach of assuming constant radiative propertiesfor each internally mixed mode They further indicate thatthe aerosol radiative effects are altered by microphysical in-teractions of the different aerosol cycles

The simulated global annual mean AOD at 550 nm in-creases from 015 at pre-industrial times to 022 around theyear 2000 The present day values are somewhat larger thansatellite retrieved estimates of MODIS (2000ndash2004 globalmean of 020) and MISR (2000ndash2004 global mean of 021)However it has been shown that these satellite retrievals ex-hibit a positive bias on a local basis The simulated valuesare also higher than a year 2000 reference simulation withECHAM5ndashHAM that shows a better agreement with the re-mote sensing data on a regional basis This overestimationcan be attributed to stronger mineral dust emissions in thefree climate mode compared to the nudged reference simu-lation as well as to stronger anthropogenic emissions in the



Aerosol Optical Depths

1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]

AODAOD fineAOD coarseAbs-AODAbs-AOD fineAbs-AOD coarse

Fig 11 Global mean total tropospheric aerosol optical depth (red)and absorption optical depth (black) at 550 nm from 1860ndash2100Also given are fine mode (dashed) and coarse mode (dotted) values

NIES emission compilation compared to the AeroCom emis-sion inventory applied in the reference simulation Frompresent day conditions to the pollution peak around 2020AOD is projected to increase to 026 Despite the signifi-cant reduction of sulfate thereafter AOD shows only a weakdecrease as the continuing increase in carbonaceous aerosolis compensating The anthropogenic enhancement of AOD isattributable to an increase in fine mode AOD However morethan a third of the simulated global mean fine mode opticaldepth of the year 2000 is already present under natural con-ditions contradicting the assumption that all fine mode AODis attributable to anthropogenic activities This introduces asignificant uncertainty to remote sensing derived estimates ofthe anthropogenic contribution to the aerosol radiative effects(eg Christopher and Zhang 2004 Kaufman et al 2005)An integrated approach is required combining the strengthsof remote sensing and global modelling to reduce the re-maining large uncertainties

The simulated anthropogenic top of the atmosphere clear-sky short-wave direct aerosol radiative perturbation intensi-fies from pre-industrial times reaching aboutminus11 Wmminus2

around 2020 Although AOD remains at a relative high levelafter 2050 DARP weakens tominus06 Wmminus2 attributable to anincrease in atmospheric absorption owing to the continuedincrease in the BC burden The onset of the anthropogenicnegative DARP in combination with increasing volcanic ac-tivity between about 1950 and 1970 contribute to the wellsimulated observed small trend in global surface tempera-tures during that period despite increasing greenhouse gasforcing

To recapitulate our results from a transient coupledAOGCM climate simulation from 1860 to 2100 with an em-bedded microphysical aerosol module show distinct alter-ations of the aerosol system on global and regional scalesover the integration period Aerosol residence-times aging-times size composition and mixing state undergo non-negligible variations As a consequence their radiative prop-

Direct Aerosol Radiative Perturbation

1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD

Fig 12 Global mean total aerosol short-wave clear-sky direct ra-diative perturbation (red) and stratospheric volcanic aerosol opticaldepth at 550 nm (black dashed) from 1860ndash2100 with respect to the1860ndash1870 mean

erties and effects and ultimately their climatic impact can-not be estimated solely based on changes of the global meanemissions

Large uncertainties in particular with respect to the futureevolution of the aerosol system remain Aerosol emission in-ventories even for present day conditions based on largelywell determined fuel use data are highly uncertain particu-larly for the carbonaceous compounds These uncertaintiespropagate into the future emission scenarios and add to theirlarge uncertainties regarding economic population techno-logical and legislative developments So one key outcomeof this study are the demonstrated modifications of aerosolparameters that have previously been assumed constant ndash un-der one possible realisation of greenhouse gas aerosol andaerosol precursor emissions Additional feedbacks of theaerosol cycles with other compartments of the earth systemsuch as aerosol effects on vegetation via fertilisation and so-lar dimming the effect of changing vegetation on the surfaceemissions of dust and organic matter as well as feedbackswith the atmospheric chemistry are also likely to affect theevolution of the aerosol system and will be subject of futureresearch activities

AcknowledgementsThis research was supported by the GermanMinistry for Education and Research (BMBF) under the DEKLIMProject and by the European Community under the ENSEMBLESProject The simulations were performed on the NEC SX-6 su-percomputer of the German High Performance Computing Centrefor Climate- and Earth System Research in Hamburg We wouldlike to thank T Nozawa (National Institute for EnvironmentalStudies Japan) and T Takemura (Kyushu University Japan) forproviding the aerosol emission dataset and their support Manythanks to S Kinne who helped with the AOD evaluation andinternally reviewed the manuscript together with D Banse Wewould also like to thank I Fischer-Bruns for helpful discussionsand M Werner (MPI-Biogeochemistry Jena) for his supportwith the dust source The continuous support of our colleaguesL Kornblueh U Schulzweida U Schlese and R Brokopf wasgreatly appreciated



Edited by S Martin

References

Adams P J Seinfeld J H and Koch D M Global concen-trations of tropospheric sulfate nitrate and ammonium aerosolsimulated in a general circulation model J Geophys Res 10413 791ndash13 824 1999

Albrecht B A Aerosols cloud microphysics and fractionalcloudiness Science 245 1227ndash1230 1989

Andreae M O Global biomass burning Atmosphere climaticand biospheric implications chap Biomass burning Its historyuse and distribution and its impact on environmental quality inglobal climate 15ndash42 MIT Press Cambridge MA 1991

Angstrom A Atmospheric turbidity global illumination and plan-etary albedo of the earth Tellus 14 435ndash450 1962

Barth M C and Church A T Regional and global distri-butions and lifetimes of sulfate aerosols from Mexico Cityand southeast China J Geophys Res 104 30 231ndash30 240doi1010291999JD900 809 1999

Christopher S A and Zhang J Cloud-free shortwave aerosolradiative effect over oceans Strategies for identifying anthro-pogenic forcing from Terra satellite measurements GeophysRes Lett 31 L18 101 doi1010292004GL020 510 2004

Cooke W F Koffi B and Gregoire J-M Seasonality of vege-tation fires in Africa from remote sensing data and application toa global chemistry model J Geophys Res 101 21 051ndash21 0651996

Cooke W F Liousse C Cachier H and Feichter J Con-struction of a 1times1 fossil fuel emission data set for carbona-ceous aerosol and implementation and radiative impact in theECHAM4 model J Geophys Res 104 22 137ndash22 162 1999

Dentener F Kinne S Bond T Boucher O Cofala J GenerosoS Ginoux P Gong S Hoelzemann J J Ito A Marelli LPenner J E Putaud J-P Textor C Schulz M van der WerfG R and Wilson J Emissions of primary aerosol and precur-sor gases in the years 2000 and 1750 prescribed data-sets forAeroCom Atmos Chem Phys Discuss 6 2703ndash2763 2006httpwwwatmos-chem-phys-discussnet627032006

Feichter J Kjellstrom E Rodhe H Dentener F Lelieveld Jand Roelofs G-J Simulation of the tropospheric sulfur cycle ina global climate model Atmos Environ 30 1693ndash1707 1996

Fouquart Y and Bonnel B Computations of solar heating of theearthrsquos atmosphere A new parameterization Beitr Phys At-mos 53 35ndash62 1980

Graf H-F Feichter J and Langmann B Volcanic sulfur emis-sions Estimates of source strength and its contribution to theglobal sulfate distribution J Geophys Res 102 10 727ndash10 7381997

Graszligl H Albedo reduction and radiative heating of clouds byabsorbing aerosol particles Contributions Atmospheric Physics48 199ndash210 1975

Guenther A Hewitt C N Erickson D Fall R Geron CGraedel T Harley P Klinger L Lerdau M McKay W APierce 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

Hansen J Nazarenko L Ruedy R Sato M Willis J GenioA D Koch D Lacis A Lo K Menon S Novakov TPerlwitz J Russell G Schmidt G A and Tausnev N Earthrsquosenergy imbalance confirmation and implications Science 3081431ndash1435 doi101126science1110252 2005

Hansen J Sato M and Ruedy R Radiative forcing and climateresponse J Geophys Res 102 6831ndash6864 1997

Holben B N Tanre D Smirnov A Eck T F Slutsker IAbuhassan N Newcomb W W Schafer J S Chatenet BLavenu F Kaufman Y J Castle J V Setzer A MarkhamB Frouin D C R Halthore R Karneli A OrsquoNeill N TPietras C Pinker R T Voss K and Zibordi G An emergingground-based aerosol climatology Aerosol optical depth fromAERONET J Geophys Res 106 12 067ndash12 098 2001

Ignatov A and Stowe L Aerosol retrievals from individualAVHRR channels part I retrieval algorithm and transition fromdave to 6S radiative transfer model J Atmos Sci 59 313ndash3342002a

Ignatov A and Stowe L Aerosol retrievals from individualAVHRR channels part II quality control probability distribu-tion functions information content and consistency checks ofretrievals J Atmos Sci 59 335ndash362 2002b

Ito A and Penner J E Historical emissions of carbonaceousaerosols from biomass and fossil fuel burning for the pe-riod 1870ndash2000 Global Biogeochem Cycles 19 GB2028doi1010292004GB002 374 2005

Jacobson M Z Control of fossil-fuel particulate black car-bon and organic matter possibly the most effective methodof slowing global warming J Geophys Res 107 4410doi1010292001JD001 376 2002

Johns T C Gregory J M Ingram W J Johnson C E JonesA Lowe J A Mitchell J F B Roberts D L Sexton DM H Stevenson D S Tett S F B and Woodage M J An-thropogenic climate change for 1860 to 2100 simulated with theHadCM3 model under updated emissions scenarios Clim Dyn20 583ndash612 doi101007s00382-002-0296-y 2003

Johnson K Gordon R and Coale K What controls dissolvediron concentrations in the world ocean Marine Chemistry 57137ndash161 1997

Jungclaus J Botzet M Haak H Keenlyside N Luo JLatif M Marotzke J Mikolajewicz U and Roeckner EOcean circulation and tropical variability in the coupled modelECHAM5MPI-OM J Clim in press 2006

Kaufman Y J Tanre D Remer L A Vermote E F Chu Aand Holben B N Operational remote sensing of troposphericaerosol over land from EOS moderate resolution imaging spec-troradiometer J Geophys Res 102 17 051ndash17 068 1997

Kaufman Y J Boucher O Tanre D Chin M Remer L Aand Takemura T Aerosol anthropogenic component esti-mated from satellite data Geophys Res Lett 32 L17 804doi1010292005GL023 125 2005

Khairoutdinov M and Kogan Y A new cloud physics parameter-ization in a Large-Eddy simulation model of marine stratocumu-lus Mon Wea Rev 128 229ndash243 2000

Kloster S Feichter J Maier-Reimer E Six K Stier P andWetzel P DMS cycle in the marine ocean-atmosphere system -a global model study Biogeosci 3 29ndash51 2006

Krivova N and Solanki S Solar variability and global warming astatistical comparison since 1850 Adv Space Res 34 361ndash364



2004Lefohn A S Husar J D and Husar R B Estimating histori-

cal anthropogenic global sulfur emission patterns for the period1850ndash1990 Atmos Environ 33 3435ndash3444 1999

Levy R C Remer L A Martins J V Kaufman Y J Plana-fattori A Redemann J and Wenny B Evaluation of theMODIS aerosol retrievals over ocean and land during CLAMSJ Atmos Sci 62 974ndash991 2005

Liepert B G Feichter J Lohmann U and RoecknerE Can aerosols spin down the water cycle in a warmerand moister world Geophys Res Lett 31 L06 207doi1010292003GL019 060 2004

Likens G E and Bohrmann F H Acid rain a serious regionalenvironmental problem Science New Series 184 1176ndash11791974

Lin H and Leaitch R Development of an in-cloud aerosol acti-vation parameterization for climate modelling in WMO Work-shop on measurement of cloud properties for forcasts of weather328ndash335 World Meteorological Organization Geneva 1997

Lin S J and Rood R B Multidimensional flux form semi-Lagrangian transport Mon Wea Rev 124 2046ndash2068 1996

Liu Y Sarnat J A Coull B A Koutrakis P and Ja-cob D J Validation of Multiangle Imaging Spectrora-diometer (MISR) aerosol optical thickness measurements us-ing Aerosol Robotic Network (AERONET) observations overthe contiguous United States J Geophys Res 109 D06 205doi1010292003JD0039 810 2004

Lohmann U A glaciation indirect aerosol effect causedby soot aerosols Geophys Res Lett 29 1052doi1010292001GL014 357 2002

Lohmann U and Karcher B First interactive simulationsof cirrus clouds formed by homogeneous freezing in theECHAM general circulation model J Geophys Res 107 4105doi1010292001JD000 767 2002

Lohmann U and Roeckner E Design and performance of a newcloud microphysics scheme developed for the ECHAM4 generalcirculation model Clim Dyn 12 557ndash572 1996

Lohmann U Feichter J Chuang C C and Penner J E Pre-dicting the number of cloud droplets in the ECHAM-GCM JGeophys Res 104 9169ndash9198 1999

Maier-Reimer E Kriest I Segschneider J and Wetzel P TheHAMburg Ocean Carbon Cycle Model HAMOCC51 ndash Tech-nical description release 11 Reports on Earth System Science14 Max Planck Institute for Meteorology Hamburg Germanyavailable fromhttpwwwmpimetmpgde 50 pp 2005

Marsland S J Haak H Jungclaus J H Latif M and RoskeF The Max-Planck-Institute global oceansea ice model withorthogonal curvilinear coordinates Ocean Modelling 5 91ndash1272003

Martin J H and Fitzwater S E Iron deficiency limits phyto-plankton growth in the northeast Pacific subarctic Nature 331341ndash343 1988

Martonchik J Diner D Crean K and Bull M Regional aerosolretrieval results from MISR IEEE Trans Geosci Remote Sens40 1520ndash1531 2002

Martonchik J Diner D Kahn R Gaitley B and Hol-ben B Comparison of MISR and AERONET aerosol opti-cal depths over desert sites Geophys Res Lett 31 L16 102doi1010292004GL019 807 2004

McAvaney B J Covey C Joussaume S Kattsov V Kitoh AOgana W Pitman A J Weaver A J Wood R A and ZhaoZ-C Climate change 2001 the scientific basis chap Modelevaluation 471ndash523 Cambridge University Press 2001

McCormic R A and Ludwig J H Climate modifications by at-mospheric aerosols Science 156 1358ndash1359 1967

Meehl G A Washington W M III D J E Briegleb B P andJaumann P J Climate change from increased CO2 and directand indirect effects of sulfate aerosols Geophys Res Lett 233755ndash3758 doi10102996GL03 478 1996

Menon S Hansen J Nazarenko L and Luo Y Climate effectsof black carbon aerosols in China and India Science 297 2250ndash2253 2002

Mitchell J F B Johns T C Gregory J T and Tett S F BClimate response to increasing levels of greenhouse gases andsulfate aerosols Nature 376 501ndash504 1995

Mlawer E J Taubman S J Brown P D Iacono M J andClough S A Radiative transfer for inhomogeneous atmo-spheres RRTM a validated correlated-k model for the long-wave J Geophys Res 102 16 663ndash16 682 1997

Morcrette J-J Clough S A Mlawer E J and Iacono M JImpact of a validated radiative transfer scheme RRTM on theECMWF model climate and 10-day forecasts Technical Memo-randum 252 ECMWF Reading UK 48 pp 1998

Nakicenovic N Alcamo J Davis G de Vries H Fenhann JGaffin S Gregory K Grubler A Jung T Kram T RovereE L Michaelis L Mori S Morita T Papper W Pitcher HPrice L Riahi K Roehrl A Rogner H-H Sankovski ASchlesinger M Shukla P Smith S Swart R van RooijenS Victor N and Dadi Z Special Report on Emissions Sce-narios Intergovernmental Panel on Climate Change CambridgeUniversity Press Cambridge UK 599 pp 2000

Nordeng T E Extended versions of the convective parameteriza-tion scheme at ECMWF and their impact on the mean and tran-sient activity of the model in the tropics Technical Memorandum206 ECMWF Reading UK 42 pp 1994

Okin G Mahowald N Chadwick O and Artaxo P Impactof desert dust on the biogeochemistry of phosphorus in ter-restrial ecosystems Global Biogeochem Cycles 18 GB2005doi1010292003GB002 145 2004

Penner J E Andreae M Annegarn H Barrie L Feichter JHegg D Jayaraman A Leaitch R Murphy D Nganga Jand Pitari G Climate change 2001 the scientific basis chapAerosols their direct and indirect effects pp 298ndash248 Cam-bridge University Press 2001

Pham M Boucher O and Hauglustaine D Changes in atmo-spheric sulfur burdens and concentrations and resulting radiatveforcings under IPCC SRES emission scenarios for 1990-2100J Geophys Res 110 D06 112 doi1010292004JD005 1252005

Rao S Riahi K and Kaarle Kupiainen Z K Long-term sce-narios for black and organic carbon emissions EnvironmentalSciences 2 doi10108015693430500397228 2005

Roeckner E Bengtsson L Feichter J Lelieveld J andHRodhe Transient climate change simulations with a coupledatmosphere-ocean GCM including the tropospheric sulfur cycleJ Clim 12 3004ndash3032 1999

Roeckner E Baeuml G Bonventura L Brokopf R EschM Giorgetta M Hagemann S Kirchner I Kornblueh



L Manzini E Rhodin A Schlese U Schulzweida Uand Tompkins A The atmospheric general circulation modelECHAM5 PART I Model description Report 349 Max PlanckInstitute for Meteorology Hamburg Germany available fromhttpwwwmpimetmpgde 2003

Roeckner E Stier P Feichter J Kloster S Esch Mand Fischer-Bruns I Impact of carbonaceous aerosol forc-ing on regional climate change Clim Dyn Online Firstdoi101007s00 382ndash006ndash0147ndash3 2006

Sato M Hansen J McCormick M and Pollack J Stratosphericaerosol optical depht 1850ndash1990 J Geophys Res 98 22 987ndash22 994 1993

Schaap M Van Der Gon H A C D Dentener F J VisschedijkA J H Van Loon M ten Brink H M Putaud J-P Guil-laume B Liousse C and Builtjes P J H Anthropogenicblack carbon and fine aerosol distribution over Europe J Geo-phys Res 109(D18) 207 doi1010292003JD004 330 2004

Schulz M de Leeuw G and Balkanski Y Emission of atmo-spheric trace compounds chap Sea-salt aerosol source functionsand emissions pp 333ndash359 Ed Kluwer 2004

Six K D and Maier-Reimer E Effects of plankton dynamicson seasonal carbon fluxes in an ocean general circulation modelGlobal Biogeochem Cyc 10 559ndash583 1996

Solanki S and Krivova N Can solar variability explainglobal warming since 1970 J Geophys Res 108 1200doi1010292002JA009 753 2003

Stier P Feichter J Kinne S Kloster S Vignati E WilsonJ Ganzeveld L Tegen I Werner M Schulz M Balkan-ski Y Boucher O Minikin A and Petzold A The aerosol-climate model ECHAM5-HAM Atmos Chem Phys 5 1125ndash1165 2005httpwwwatmos-chem-physnet511252005

Stier P Feichter J Kloster S Vignati E and Wilson JEmission-induced nonlinearities in the global aerosol systemndash Results from the ECHAM5-HAM aerosol-climate model JClim in press 2006

Streets D G Bond T C Lee T and Jang C On the future ofcarbonaceous aerosol emissions J Geophys Res 109 D24212doi1010292004JD004 902 2004

Swap R Garstang M Greco S Talbot R and Kallberg PSaharan dust in the amazon basin Tellus B 44 133ndash149 1992

Takemura T Okamoto H Maruyama Y Numaguti A Hig-urashi A and Nakajima T Tropospheric global three-dimensional simulation of aerosol optical thickness distributionof various origins J Geophys Res 105 17 853ndash17 873 2000

Takemura T Nozawa T Emori S Nakajima T Y and Naka-jima T Simulation of climate response to aerosol direct and in-direct effects with aerosol transport-radiation model J GeophysRes 110(D02) 202 doi1010292004JD005 029 2005

Tanre D Kaufman Y J Herman M and Mattoo S Remotesensing of aerosol properties over oceans using the MODISEOSspectral radiances J Geophys Res 102 16 971ndash16 988 1997

Tegen I Harrison S P Kohfeld K Prentice I C Coe M andHeimann M Impact of vegetation and preferential source areason global dust aerosol Results from a model study J GeophysRes 107 4576ndash4597 2002

Tegen I Werner M Harrison S P and Kohfeld K E Rela-tive importance of climate and land use in determining presentand future global soil dust emission Geophys Res Lett 31L05 105 doi1010292003GL019 216 2004

Tett S F B Jones G S Stott P A Hill D C Mitchell JF B Allen M R Ingram W J Johns T C Johnson C EJones A Roberts D L Sexton D M H and WoodageM J Estimation of natural and anthropogenic contributions totwentieth century temperature change J Geophys Res 107doi1010292000JD000 028 2002

Tiedtke M A comprehensive mass flux scheme for cumulusparameterization in large scale models Mon Wea Rev 1171779ndash1800 1989

Timmreck C and Schulz M Significant dust simulationdifferences in nudged and climatological operation modeof the AGCM ECHAM J Geophys Res 109 D13 202doi1010292003JD004 381 2004

Tompkins A A prognostic parameterization for the subgrid-scalevariability of water vapor and clouds in large-scale models andits use to diagnose cloud cover J Atmos Sci 59 1917ndash19422002

Torres O Bhartia P Herman J Sinyuk A and Holben B Along term record of aerosol optical thickness from TOMS obser-vations and comparison to AERONET measurements J AtmosSci 59 398ndash413 2002

Twomey S Pollution and the planetary albedo Atmos Environ8 1251ndash1256 1974

Twomey S The influence of pollution on the shortwave albedo ofclouds J Atmos Sci 34 1149ndash1152 1977

Unger N Shindell D T Koch D M and Streets D G Crossinfluences of ozone and sulfate precursor emissions changes onair quality and climate Proc Natl Acad Sci USA 103 4377ndash4380 doi101073pnas0508769103 2006

Vignati E Wilson J and Stier P M7 a size resolved aerosolmixture module for the use in global aerosol models J GeophysRes 109(D22) 202 doi1010292003JD004 485 2004

Wannikhof R Relationship between wind speed and gas exchangeover the ocean J Geophys Res 97 7373ndash7382 1992

Zhang J Christopher S Remer L and Kaufman Y short-wave aerosol radiative forcing over cloud-free oceans from Terra2 seasonal and global distributions J Geophys Res 110D10S24 doi1010292004JD005 009 2005



contribution to the radiative effects As natural aerosols aredominated by primary particles in the larger size fraction thefine fraction is typically used as proxy for the anthropogeniccontribution to the aerosol radiative perturbation (Christo-pher and Zhang 2004)

Global aerosol models can contribute to increase the un-derstanding about the complex global aerosol system forpast present and future conditions Furthermore they per-mit to identify the effects of specific aerosol components andaerosol sources natural or anthropogenic on the global cli-mate system

In early transient coupled atmosphere-ocean global cir-culation model (AOGCM) climate simulations the radia-tive impact of anthropogenic aerosols has been neglectedLater modified surface albedos as proxy of the radiative ef-fects of sulfate aerosols have been included (Mitchell et al1995 Meehl et al 1996) The consideration of prognos-tic sulfur cycle schemes in coupled AOGCM climate sim-ulations remains the exception (Roeckner et al 1999 Tettet al 2002 Johns et al 2003) While other anthropogenicaerosol components in particular of carbonaceous aerosolshave received considerable attention (egPenner et al 2001Menon et al 2002 Jacobson 2002) only recently they arebecoming included in transient coupled AOGCM simula-tions (egHansen et al 2005 Takemura et al 2005) Therelative importance of carbonaceous aerosols is projected toincrease according to recent emission scenarios (eg SRESNakicenovic et al 2000) While according to the widelyused SRES A1B scenario anthropogenic SO2 emissions asthe main sulfate precursor are projected to peak in the year2020 the emissions of the carbonaceous aerosols are ex-pected to increase up to the year 2100 Therefore the limi-tation to sulfate aerosols in most previous transient AOGCMclimate simulations is likely to draw an incomplete picturein particular with respect to future climate projections

Here we present results from a transient climate simulationfrom 1860 to 2100 with an evolving earth system model con-sisting of interactively coupled atmosphere and ocean GCMswith embedded atmospheric aerosol cycles and ocean bio-geochemistry In addition to the traditional physical couplingof the atmosphere and ocean models also the atmosphericand oceanic biogeochemical cycles are coupled interactivelyThe microphysical aerosol module HAM (Stier et al 2005)has a prognostic representation of the major global aerosolcomponents with prognostic treatment of their microphysi-cal state and interactions The aerosol components consid-ered are sulfate (SU) black carbon (BC) particulate organicmatter (POM) sea salt (SS) and mineral dust (DU) Aerosolcomposition size distribution and mixing state are prognos-tic Natural emissions of mineral dust sea salt and dimethyl-sulfide (DMS) are calculated interactively Future green-house gas concentrations as well as anthropogenic aerosoland aerosol-precursor emissions are prescribed based on theSRES A1B scenario

The focus of this study is to analyse the transient evolutionof the simulated global aerosol system from 1860 to 2100Other aspects of the results such as the regional climate im-pact of carbonaceous aerosols (Roeckner et al 2006 hence-forth R2006) or the interactive DMS cycle (Kloster et al1henceforth K2006) are addressed in accompanying publi-cations

Section2 describes the setup of the earth system modelThe analysis of the simulated aerosol evolution from 1860 to2100 is presented in Sect3 Section4 concludes the discus-sion

2 Model description

In this study we use a sub-model of the emerging MaxPlanck Institute ndash Earth System Model (MPI-ESM) consist-ing of the following interactively coupled models the atmo-spheric general circulation model ECHAM5 (Roeckner et al2003) the ocean general circulation model MPI-OM (Mars-land et al 2003) the atmospheric aerosol module HAM(Stier et al 2005) and the ocean biogeochemistry moduleHAMOCC5 (Maier 2005) The atmospheric and oceanicbiogeochemical cycles are coupled by accounting for de-position of mineral dust and subsequent iron dissolution asmicro-nutrient for the ocean biogeochemistry and by emit-ting DMS produced by phytoplankton from the ocean sur-face to the atmosphere A detailed description and evaluationof the coupled ECHAM5 ndash HAM system is given inStieret al(2005) and the coupled ECHAM5 ndash HAM ndash MPI-OMndash HAMOCC5 system is evaluated inKloster et al(2006)

21 The atmosphere GCM ECHAM5

The atmospheric GCM ECHAM5 (Roeckner et al 2003)is the fifth-generation climate model developed at the MaxPlanck Institute for Meteorology ECHAM5 solves prognos-tic equations for vorticity divergence surface pressure andtemperature expressed in terms of spherical harmonics witha triangular truncation Non-linear processes and the phys-ical parameterisations are solved on a corresponding Gaus-sian grid Water vapour cloud liquid water cloud ice andtrace components are transported in grid-point space with aflux form semi-Lagrangian transport scheme (Lin and Rood1996) ECHAM5 contains a microphysical cloud scheme(Lohmann and Roeckner 1996) with prognostic equationsfor cloud liquid water and ice Cloud cover is predictedwith a prognostic-statistical scheme solving equations forthe distribution moments of total water (Tompkins 2002)Convective clouds and convective transport are based on themass-flux scheme ofTiedtke (1989) with modifications by

1Kloster S Feichter J Maier-Raimer E Roeckner E Wet-zel P Stier P Six K D and Esch M Response of DMS in theocean and atmosphere to global warming Max Planck Institute forMeteorology Hamburg Germany submitted 2006








2 MBC2 MPOM

2 N5 MBC5 MPOM


3 MBC3 MPOM

3 MSS3 MDU

3 N6 MDU6

Coarse05ltr N4 MSU

4 MBC4 MPOM

4 MSS4 MDU

4 N7 MDU7


















26 Simulation setup















3 Results

31 Emissions




SO2

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

120

140

Em

issi

ons

[Tg(

S)]


DMS

1860 1900 1950 2000 2050 2100Year

20

21

22

23

24

25

26

Em

issi

ons

[Tg(

S)]

σnorm = 001

Black Carbon

1860 1900 1950 2000 2050 2100Year

0

10

20

30

40

Em

issi

ons

[Tg]


Sea Salt

1860 1900 1950 2000 2050 2100Year

5800

6000

6200

6400

6600

Em

issi

ons

[Tg]

σnorm = 001


1860 1900 1950 2000 2050 2100Year

0

50

100

150

200

Em

issi

ons

[Tg]


Dust

1860 1900 1950 2000 2050 2100Year

800

1000

1200

1400

1600E

mis

sion

s [T

g]σnorm = 008












Sulfur 447 Tg (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1019 Tg (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1166 Tg (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10









Sulfate 04 Tg(S) (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 09 Tg(S) (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 12 Tg(S) (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20

POM 04 Tg (1861-1890)

0 01 02 05 1 2 5 10 20 50 100 200

POM 15 Tg (1961-1990)

0 01 02 05 1 2 5 10 20 50 100 200

POM 35 Tg (2021-2050)

0 01 02 05 1 2 5 10 20 50 100 200









Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

σnorm = 002

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

σnorm = 009

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S








Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]




1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day












Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References




































































































2 MBC2 MPOM

2 N5 MBC5 MPOM


3 MBC3 MPOM

3 MSS3 MDU

3 N6 MDU6

Coarse05ltr N4 MSU

4 MBC4 MPOM

4 MSS4 MDU

4 N7 MDU7


















26 Simulation setup















3 Results

31 Emissions




SO2

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

120

140

Em

issi

ons

[Tg(

S)]


DMS

1860 1900 1950 2000 2050 2100Year

20

21

22

23

24

25

26

Em

issi

ons

[Tg(

S)]

σnorm = 001

Black Carbon

1860 1900 1950 2000 2050 2100Year

0

10

20

30

40

Em

issi

ons

[Tg]


Sea Salt

1860 1900 1950 2000 2050 2100Year

5800

6000

6200

6400

6600

Em

issi

ons

[Tg]

σnorm = 001


1860 1900 1950 2000 2050 2100Year

0

50

100

150

200

Em

issi

ons

[Tg]


Dust

1860 1900 1950 2000 2050 2100Year

800

1000

1200

1400

1600E

mis

sion

s [T

g]σnorm = 008












Sulfur 447 Tg (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1019 Tg (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1166 Tg (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10









Sulfate 04 Tg(S) (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 09 Tg(S) (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 12 Tg(S) (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20

POM 04 Tg (1861-1890)

0 01 02 05 1 2 5 10 20 50 100 200

POM 15 Tg (1961-1990)

0 01 02 05 1 2 5 10 20 50 100 200

POM 35 Tg (2021-2050)

0 01 02 05 1 2 5 10 20 50 100 200









Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

σnorm = 002

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

σnorm = 009

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S








Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]




1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day












Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References






































































































26 Simulation setup















3 Results

31 Emissions




SO2

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

120

140

Em

issi

ons

[Tg(

S)]


DMS

1860 1900 1950 2000 2050 2100Year

20

21

22

23

24

25

26

Em

issi

ons

[Tg(

S)]

σnorm = 001

Black Carbon

1860 1900 1950 2000 2050 2100Year

0

10

20

30

40

Em

issi

ons

[Tg]


Sea Salt

1860 1900 1950 2000 2050 2100Year

5800

6000

6200

6400

6600

Em

issi

ons

[Tg]

σnorm = 001


1860 1900 1950 2000 2050 2100Year

0

50

100

150

200

Em

issi

ons

[Tg]


Dust

1860 1900 1950 2000 2050 2100Year

800

1000

1200

1400

1600E

mis

sion

s [T

g]σnorm = 008












Sulfur 447 Tg (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1019 Tg (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1166 Tg (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10









Sulfate 04 Tg(S) (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 09 Tg(S) (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 12 Tg(S) (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20

POM 04 Tg (1861-1890)

0 01 02 05 1 2 5 10 20 50 100 200

POM 15 Tg (1961-1990)

0 01 02 05 1 2 5 10 20 50 100 200

POM 35 Tg (2021-2050)

0 01 02 05 1 2 5 10 20 50 100 200









Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

σnorm = 002

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

σnorm = 009

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S








Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]




1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day












Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References







































































































3 Results

31 Emissions




SO2

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

120

140

Em

issi

ons

[Tg(

S)]


DMS

1860 1900 1950 2000 2050 2100Year

20

21

22

23

24

25

26

Em

issi

ons

[Tg(

S)]

σnorm = 001

Black Carbon

1860 1900 1950 2000 2050 2100Year

0

10

20

30

40

Em

issi

ons

[Tg]


Sea Salt

1860 1900 1950 2000 2050 2100Year

5800

6000

6200

6400

6600

Em

issi

ons

[Tg]

σnorm = 001


1860 1900 1950 2000 2050 2100Year

0

50

100

150

200

Em

issi

ons

[Tg]


Dust

1860 1900 1950 2000 2050 2100Year

800

1000

1200

1400

1600E

mis

sion

s [T

g]σnorm = 008












Sulfur 447 Tg (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1019 Tg (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1166 Tg (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10









Sulfate 04 Tg(S) (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 09 Tg(S) (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 12 Tg(S) (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20

POM 04 Tg (1861-1890)

0 01 02 05 1 2 5 10 20 50 100 200

POM 15 Tg (1961-1990)

0 01 02 05 1 2 5 10 20 50 100 200

POM 35 Tg (2021-2050)

0 01 02 05 1 2 5 10 20 50 100 200









Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

σnorm = 002

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

σnorm = 009

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S








Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]




1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day












Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References































































































SO2

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

120

140

Em

issi

ons

[Tg(

S)]


DMS

1860 1900 1950 2000 2050 2100Year

20

21

22

23

24

25

26

Em

issi

ons

[Tg(

S)]

σnorm = 001

Black Carbon

1860 1900 1950 2000 2050 2100Year

0

10

20

30

40

Em

issi

ons

[Tg]


Sea Salt

1860 1900 1950 2000 2050 2100Year

5800

6000

6200

6400

6600

Em

issi

ons

[Tg]

σnorm = 001


1860 1900 1950 2000 2050 2100Year

0

50

100

150

200

Em

issi

ons

[Tg]


Dust

1860 1900 1950 2000 2050 2100Year

800

1000

1200

1400

1600E

mis

sion

s [T

g]σnorm = 008












Sulfur 447 Tg (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1019 Tg (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1166 Tg (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10









Sulfate 04 Tg(S) (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 09 Tg(S) (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 12 Tg(S) (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20

POM 04 Tg (1861-1890)

0 01 02 05 1 2 5 10 20 50 100 200

POM 15 Tg (1961-1990)

0 01 02 05 1 2 5 10 20 50 100 200

POM 35 Tg (2021-2050)

0 01 02 05 1 2 5 10 20 50 100 200









Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

σnorm = 002

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

σnorm = 009

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S








Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]




1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day












Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References































































































Sulfur 447 Tg (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1019 Tg (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfur 1166 Tg (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10


0 0005 001 002 005 01 02 05 1 2 5 10









Sulfate 04 Tg(S) (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 09 Tg(S) (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 12 Tg(S) (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20

POM 04 Tg (1861-1890)

0 01 02 05 1 2 5 10 20 50 100 200

POM 15 Tg (1961-1990)

0 01 02 05 1 2 5 10 20 50 100 200

POM 35 Tg (2021-2050)

0 01 02 05 1 2 5 10 20 50 100 200









Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

σnorm = 002

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

σnorm = 009

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S








Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]




1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day












Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References































































































Sulfate 04 Tg(S) (1861-1890)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 09 Tg(S) (1961-1990)

0 001 002 005 01 02 05 1 2 5 10 20

Sulfate 12 Tg(S) (2021-2050)

0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20


0 001 002 005 01 02 05 1 2 5 10 20

POM 04 Tg (1861-1890)

0 01 02 05 1 2 5 10 20 50 100 200

POM 15 Tg (1961-1990)

0 01 02 05 1 2 5 10 20 50 100 200

POM 35 Tg (2021-2050)

0 01 02 05 1 2 5 10 20 50 100 200









Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

σnorm = 002

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

σnorm = 009

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S








Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]




1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day












Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References































































































Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

Sulfate

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

10

12

14

Bur

den

[Tg(

S)]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

Black Carbon

1860 1900 1950 2000 2050 2100Year

00

02

04

06

08

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

σnorm = 002

Sea Salt

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]


1860 1900 1950 2000 2050 2100Year

0

1

2

3

4

5

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

σnorm = 009

Dust

1860 1900 1950 2000 2050 2100Year

0

5

10

15

20

25

30

Bur

den

[Tg]

30N - 90N

0 - 30N

30S - 0

90S - 30S








Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]




1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day












Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References































































































Residence Time

1860 1900 1950 2000 2050 2100Year

3

4

5

6

7

8

9

SU

BC

PO

M D

U [d

ays]

060

065

070

075

080

085

090

SS

[day

s]




1860 1900 1950 2000 2050 2100Year

00

05

10

15

20

25

30

BC

and

PO

M [d

ays]

00

20

40

60

80

100

DU

[day












Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References































































































Sulfate

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Dust

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Black Carbon

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]

















4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References





































































































4 Conclusions




Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References































































































Aitken Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100M

assf

ract

ion

[]


Aitken Insoluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]



1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]


Coarse Soluble

1860 1900 1950 2000 2050 2100Year

0

20

40

60

80

100

Mas

sfra

ctio

n [

]






1860 1900 1950 2000 2050 2100Year

000

002

004

006

008

010

Co-

Sin

gle

Sca

tterin

g A

lbed

o [1

]







AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References
































































































AOD (550nm) 01 02 03 04










1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References
































































































1860 1900 1950 2000 2050 2100Year

000

005

010

015

020

025

030

AO

D [1

]

0000

0002

0004

0006

0008

0010

Abs

orpt

ion

AO

D [1

]








1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]

00

01

02

0000

Str

atos

pher

ic A

OD

[1]


1860 1900 1950 2000 2050 2100Year

0

-2

-4

-6

DA

RP

[Wm

-2]

DARPAOD







Edited by S Martin

References































































































Edited by S Martin

References






























































































The evolution of the global aerosol system in a transient ...

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