Decreasing Particle Number Concentrations in a Warming Atmosphere and Implications F

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ects ff ect cloud ff Chemistry and Physics Discussions Atmospheric 4 , and R. M. Yantosca 3 27914 27913 , J. A. Ogren 2 ect the Earth’s energy budget directly by scattering solar radi- ff , R. P. Turco 1 , G. Luo 1 This discussion paper is/has beenand under Physics review (ACP). for Please the refer journal to Atmospheric the Chemistry corresponding final paper in ACP if available. Atmospheric Sciences Research Center, StateDepartment University of of New Atmospheric York and at Oceanic Albany, USA Sciences, University of CaliforniaGlobal at Monitoring Los Division Angeles, (GMD), EarthSchool System of Research Engineering Laboratory and (ESRL), Applied NOAA, Sciences, USA Harvard University, USA 2007). A majorquently source observed of in the atmospheric atmosphere CCN (Pierce and is Adams, the 2007; Makkonen secondary et particle al., 2009; formation fre- 1 Introduction Atmospheric particles a ation, and indirectly by actingoptical as properties cloud and condensation influence nucleiaerosol precipitation (CCN), indirect (e.g., which Twomey, radiative 1977). a forcingsents The is one magnitude of poorly of the constrained greatest in remaining uncertainties climate in models, assessing and climate repre- change (IPCC, of rising temperatures onmay imply aerosol substantial nucleation decreases in rates, futurewith tropospheric and global particle possibly abundances warming, associated on delineating DMScreases a emissions, Earth’s potentially climate significant sensitivity feedbackneeded mechanism to to that greenhouse quantify in- gas the emissions. magnitude of Further such a research feedback is process. projected increases in global temperaturesCCN, could formation significantly rates inhibit worldwide. newNOAA particle, ESRL/GMD An and baseline analysis stations of sincereveals CN the long-term concentrations 1970s decreasing and observed trends twoalso at other consistent suggests, four sites with owing since these to 1990s explained predictions. by larger the observed The basic CN nucleation analysis may mechanism, reductions be that at dimethylsulphide decreasing (DMS) remote worldwide emissions tive sites DMS-based with than cloud increasing feedback can forcing global of be the temperatures, climate implying (“CLAW”). The a combined posi- e New particle formation contributes significantly tosation the nuclei number (CN) concentration as of well conden- asradiative cloud forcing CN of (CCN), the a climate keythat factor is system. determining consistent aerosol Using with a indirect a range physics-based of nucleation field observations mechanism of aerosol formation, it is shown that Abstract Decreasing particle number concentrations in a warming atmosphere and implications F. Yu Atmos. Chem. Phys. Discuss., 11,www.atmos-chem-phys-discuss.net/11/27913/2011/ 27913–27936, 2011 doi:10.5194/acpd-11-27913-2011 © Author(s) 2011. CC Attribution 3.0 License. 1 2 USA 3 4 Received: 22 August 2011 – Accepted: 29Correspondence September to: 2011 F. Yu – ([email protected]) Published: 14 OctoberPublished 2011 by Copernicus Publications on behalf of the European Geosciences Union. 5 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). J ), further 1 − s 3 − ects of climate ff erent nucleation ff , under the condi- 1 − s 3 1–20 cm − . It should be noted that ∼ J er a more direct physical ff is very low, binary homoge- 5 cm . 0 T ects, because nucleation under = ff ], sulfate aerosols, and the indi- for IMN approaches that for BHN. J 4 values increase from insignificant J at higher ambient temperatures, but J based on several di SO 2 J ). At T 15–35 yr records of CN data, appear to 1 has been determined using a physics- is a non-linear function of temperature. − ∼ based on the IMN model are in the range s on T T J ) and sulfuric acid vapor concentrations 3 J − T to 27916 27915 J ect of temperature on nucleation rates based on ff 1 cm ∼ > reaches a significant value ( IMN J ) is a key parameter determining particle nucleation rates ( T values for BHN are greater than those for the integrated IMN J C. After ◦ ) per degree increase in ) to significant ( 1 J − 1500 publications since 1987, according to Ayers and Cainey, 2007), (other parameters fixed) have small e s ∼ T 3 − 20 % per ]) are the dominant factors controlling particle nucleation events in many re- − 4 ) (i.e., PC ect of temperature increase on global particle formation rates and 01 cm . J ff erent BHN (Vehkamaki et al., 2002) and several ternary homogeneous nucleation 0 80– SO ff concentrations 2 − ∼ Figure 2 shows a similar or larger e Ambient atmospheric temperatures ( < some unknown or yet to be established nucleation mechanisms may also contribute Compared to BHN alone,smaller IMN values yields at larger lower temperatures. PC range If of averaged over interest, the PC entiremechanism. ambient temperature a di (THN) models (Napari et al.,of 2002; Napari Yu, 2006; et Merikanto al.2006; et Merikanto al., (2002), et 2007). now al., The known 2007), THN gives to model the overestimate largest the values of THN PC rates significantly (Yu, tions considered in Fig. 1,of the values of PC decreases in such conditions is limitedneous by nucleation the (BHN) becomes ionization dominant rate. and PC When based ion-mediated nucleation (IMN)modynamic model data that (Yu, incorporates 2010) recently andmeasurements available has of ther- freshly been nucleated tested particles, againstand including detailed their Turco, multiple-instrument electrical 2011). charge state2007) The (Yu when IMN reduces therate to ionization ( binary rate homogeneous isIMN nucleation zero. rates (BHN) (Yu, are The very( percentage sensitive to change temperature in as the nucleation Physically, temperature ( Figures 1 and 2 showtheories. the dependence of In Fig. 1, the sensitivity of 2 E temperature-DMS relationship is presented here). while the associated climatelinkage, feedback have processes, never which beenmagnitude o investigated. of a Hence, positive climate in systemon this feedback particle involving study, the we nucleation influence of seek rates.first temperature to time Long from quantify six term the observationalsupport trends stations such in with a CN feedback concentrations, mechanism.data, as derived Moreover, presented a for below, may consistent the also interpretationlikely indicate to of that be the the sign positive CN of –sensitive CLAW that changes feedback process in is, is DMS global emissions warming (although might no be direct enhanced evidence through of temperature the nature of the feedback process remain2007; ambiguous (e.g., Woodhouse et Ayersering al., and CLAW 2010). Cainey, ( 2007; Invery Kloster contrast few et investigations to have al., thechange been (specifically, large carried temperature variations) number out on of to new particle quantify studies and the consid- CCN direct formation rates, e ([H gional settings (although otherrates, parameters, can such also aspast, contribute relative it in was humidity suggested determining andof that ionization nucleation dimethylsulphide future (DMS), rates; climatic thusrect warming e.g., influencing might radiative Yu, [H change forcing 2010). oceanic of(i.e., emissions the marine In CLAW stratus the hypothesis;study, clouds, however, Charlson the defining et strength a and al., even climate 1987). the sign feedback After (i.e., process negative more or than positive) two of the decades CLAW of The non-linear dependence ofters, aerosol and the nucleation influence rates ofimportant on aerosol formation key climate on atmospheric feedback CCN mechanism parame- populations,ation, involving points CCN to interactions production, a between cloud potentially particle microphysics, and nucle- climatic responses. Wang and Penner, 2009; Yu and Luo, 2009; Spracklen et al., 2010; Kazil et al., 2010). 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | C of ◦ N). The ◦ in the tropical erent regions and there are S–30 1 ff ◦ ◦ − 5 C/decade on av- . C ◦ ◦ 2 × 17 ◦ . 3–30 % for each 0 ∼ ∼ C of warming in most of the C/decade in di ◦ ◦ 75 . 0 + 400 mb in the tropics (30 2–8 % per ∼ ∼ is small, consistent with the dependence of 25 to erencing the results provides the sensitivity . ff 0 J 27918 27917 − on CN10 concentrations is relatively small in the over Antarctica and in the upper troposphere is C; di ◦ C. It is clear from Fig. 3 that, throughout most of T T ◦ to J is high and T 10 nm) agree quite well (generally within a factor of two) with ∼ ect of increasing ff 3 nm, and ∼ shown in Fig. 1. Figure 3b indicates that the regions where large decreases T ] in these regions. 4 occur extend from the surface up to on APM, with nucleation rates predicted by the IMN mechanism (Yu, 2010) and the C around the baseline climate state. In the real atmosphere, global temperature ◦ SO J + J 2 Figure 3c, d indicates that, as a result of the reduced nucleation rates, the annual Global surface temperatures have increased at a rate of Figure 3 illustrates the horizontal (i.e., averaged within a 0–2 km surface layer) and The results presented below are based on a one-year simulation (October 2004– In order to confidently assess the significance of the temperature feedback mecha- erage since the 1970s, varying from [H mean CN10 concentrations decreasetroposphere. by Similar to thethe sensitivity tropical of regions, J, and theper can maximum exceed degree decrease 10 of in %. warming CN10or The occurs (Fig. more). zonally-averaged in 3d) decreases The in are e CN10 polar largest regions in and the in tropical the tropical lower upper troposphere troposphere. (to 5 % lower troposphere, where PC in relatively lower sensitivity of a result of very cold temperatures, such that nucleation is more strongly limited by late and illustrate the impactconcentrations. of temperature change on nucleation and particle number vertical (i.e., zonally averaged) spatial distributions(PC) of in annual mean the percentage change nucleationnm rate diameter) and concentrations CN10 per the (i.e., troposphere, total annual number meanwarming. nucleation of The rates particles maximum decrease decrease can larger by reach than values exceeding 10- 30 % ity, two runs were inter-compared; oneall using temperatures baseline temperatures, incremented and by aper second 1 with change is inhomogeneous andfields, emissions, has chemistry, many etc.). The other sensitivity associated study presented changes here (meteorological is aimed to iso- 47 vertical layers in the model (surface to 0.01 hpa). To estimate temperature sensitiv- ing (Yu, 2011) taken into account. The horizontal resolution is 2 particles in the sizestudies range indicate of that 1.2–120 the nm predicted (Yularger total and than particle Luo, number 2009). concentrations (with Oura diameters previous comprehensive validation set of2009; land-, Yu et ship-, al., and 2010; aircraft- Luo based and measurements Yu, 2011). (Yu andDecember Luo, 2005, with04 the firstcondensation of 3 low months volatile secondary as organic spin-up) gases from using successive oxidation GEOS-Chem ag- v8-02- Park et al., 2004;2007; Evans Trivitayanurak et and al., 2008; Jacob, Henze 2005;key et emission al., Liao 2008; inventories et Yu (e.g., and al., Guenther Luo,2009). 2009) 2007; et The with al., Fountoukis APM up-to-date model 2006; and intion Bond Nenes, GEOS-Chem of et is secondary al., optimized particles 2007; to and Zhang accurately their simulate et growth the al., to forma- CCN sizes, with high size resolution for that can treat particle microphysicalessential. processes – Baseline especially aerosol nucleation simulationsservational and should data. growth also – be Yu is and carefully(APM) Luo validated model into (2009) against GEOS-Chem, ob- incorporated a an globalby assimilated 3-D advanced meteorological model particle data of from microphysics atmospheric the composition(GEOS-5). NASA driven Goddard The Earth GEOS-Chem Observing model Systemgroups has 5 been and developed contains and used aand by number aerosol many of research processes state-of-the-art (e.g., modules Bey treating et various al., chemical 2001; Liu et al., 2001; Martin et al., 2003; mechanisms would also beter quite controlling the sensitive saturation toclusters, vapor temperature, and pressures thus as of nucleation precursors, this rates. stability is of a pre-nucleation keynism parame- associated with new particle formation and CCN evolution, a global aerosol model to the production of new particles in the atmosphere. We expect that such nucleation 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 2 ect ff erent ff 8 nm to 4 %, and ∼ C/decade . ◦ 5 %) e 5 ∼ − < 9 %, . 11 − 0 %, . C/decade (IPCC, 2007). The ◦ emissions may also have con- 15 25 − 2 . ciency, instrument operation con- 0 E) in the latter 1970s (Gras, 1990) ffi + ◦ 9 %, . ciency diameters varying from 18 ffi and CN appear to be consistent with the − ects of temperature changes alone (Fig. 3c), T S, 144.7 ff 27920 27919 ◦ 2 %, . C/decade to 1 week, and thus CN are subject to the influence ◦ ∼ 39 25 − . erent periods were obtained using two or three di 0 ff − 15 nm). The supporting analysis provided in the Supplement shows ∼ emissions in the Eurasia and US may have contributed to incremental decreases 4 % per decade at BRW, BND, MLO, SMO, NEU and SPO, respectively. Both sur- . 2 15 yr) CN data presently available and our analysis indicates that, thanks to the rel- The observed long-term trends in CN at the 6 sites shown in Fig. 4 are generally Although there exist substantial inter-annual variations (likely as a result of El Nino, Figure 4 illustrates the monthly mean CN values observed at these six sites, along 12 nm and 17 > reduce DMS emission, in which case the feedback loop becomes positive (so-called DMS, primary particles, amongSO others). For example,in the CN changes at in BRW.tributed anthropogenic The to the reduction decreasing in trendSPO anthropogenic in sites, CN SO oceanic at DMS BND.influences At on emissions more CN are remote would MLO, theof be SMO, major oceanic expected NEU, DMS to and sulfur emissions be source, to small.feedback and global process, As climate anthropogenic mentioned remains change, earlier, and undetermined.global the hence warming response the A may sign actually number of stratify the of the CLAW recent ocean studies surface, limit indicate nutrient that supply, and thus atively long duration of thethe data, overall a decreasing few short trends periods of of CN abnormal concentrations. values do notlarger change than would be expected fromperhaps the as e a result of changes in the emissions of precursor gases (such as SO opposing observed long-termphysics trends of in nucleation and particlespheric evolution particles (Figs. have 1–3). lifetimesof of It variations should in be temperatures notedsites. and that We nucleation tropo- would rates like in toject regions emphasize that to upwind the of uncertainty values measurement of associateddition, above with derived and CN CN missing trends counting are( data. e sub- Nevertheless, Fig. 4 represents the best set of long-term − face and tropospheric air temperaturesthe trended regions upward during around the BRW, last BND,(IPCC, 3–4 MLO, 2007). decades and Temperatures increase in SMO, in inNEU some the regions, and and range SPO, decrease of ranging in from 0.15–0.55 others around trend at each of the six sites: and some periods of missing data, CN concentrations exhibit a long-term decreasing ∼ that the changeover ofon CN the counters variance is of CN likelymeasurements values to at from have these SPO had three are remote onlyan sites. included a Only unexplained in small the this increase ( most study, recent incounter as 22 yr in CN explained of 1989 in concentrations the hinders associatedtime Supplement, the series. with interpretation a of change the in entire the record CN thereinterannual as and a decadal continuous dynamics, volcanic eruptions, biomass burning, and so on) but the long-term Cape Grim CN data are notwith yet linear available. regression (LR) results.BRW, MLO, It and should SMO be during di noted thatCN 30 counters plus (with years 50 of % CN particle data detection for e Bondville, Illinois (BND) whichGerman has Antarctic station recorded Neumayer CN (NEU)Lampert, values has 2008). since measured CN To 1994. our sincetions knowledge, 1993 In marked BRW, (Weller addition, MLO, and respectively SMO, the as SPO,around A, the BND, globe and B, having NEU C, more (atallow D, than loca- one 15 E, yr to and of look CN F intotions. data in the available in long-term Fig. It the trends 3c) public should of domainCN are be that atmospheric measurements the noted particle at only that number Cape 6 concentra- the Grim stations Australian (40.7 “Atmospheric Baseline” program began led to noticeable changesbeginning in in nucleation the rates and middle/latehas CN10 1970s, collected abundances. NOAA/ESRL’s long-term Global measurements As Monitoringrow, of it Alaska Division CN happens, (BRW), (GMD) at Mauna four Loa remoteand Observatory, Hawaii baseline South (MLO), stations Pole American at Observatory Samoa Bar- (SMO), (SPO) (Bodhaine, 1983). The GMD has another site at (IPCC, 2007). According to the present results, such long-term trends should have 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3 − in re- 3 − 150 cm ∼ over the North Atlantic ) of 0.94 and a normal- elsewhere in the marine r 3 LBL values with those observed − cient ( LBL ffi 100 cm ∼ erent seasons, and averaged values ranges from several-tens cm ff over polluted regions, with the highest 3 − LBL erent points on the globe (within a factor ff in the lower boundary layer over East Asia. 3 27922 27921 − ect of warming temperatures on nucleation rates alone ff over the polar regions are generally below APM model, which generally captures the absolute val- LBL + ects of warming on nucleation rates and DMS emissions (via ect on CCN concentrations must be determined, which depends ff ff , although it is generally below 3 − ). Figure 5b compares simulated [CCN0.4] values are roughly a factor of two or more larger than corresponding levels LBL LBL ect of temperature increase on CCN concentrations ff 2 for all sites), keeping in mind limitations in the CCN0.4 data with respect to re- ∼ With the GEOS-Chem Figure 5b shows that the simulated CCN0.4 values agree reasonably well with ues as well asto the changes spatial in variations nucleation in the rates observed associated CCN with distribution, global the warming sensitivity can be investigated. [CCN0.4] in the Southern Hemisphere.bution has As large a horizontal result gradientscan of in be continental coastal seen outflows, areas, the over andexceeds [CCN0.4] some enhanced 100 cm distri- [CCN0.4] regions zones ofboundary the oceans. layer. [CCN0.4] [CCN0.4] and comparable to those over the remote oceans. the 26 sites worldwide, withized an mean overall bias correlation coe (NMB)mote of oceanic 0.78 areas %. to several-thousandsannual cm [CCN0.4] mean value exceeding 4000Due cm to large anthropogenic sources over major continents in the Northern Hemisphere, Owing to large seasonalobservations variations, are model used results for corresponding comparisonsin to with Fig. the 5b observations months define in of the Fig. the simulated 5b. ranges The of vertical monthly bars meansurface-based CCN observations values. obtained atof di gional representation and durationthe of absolute observations. values as Overall, well the as simulations the capture horizontal variations of the observed [CCN0.4] at were used for the comparisons.calculated In at the the model prescribed employedtributions supersaturation here, and based CCN concentrations on compositions, are the usingshould simulated the be particle scheme noted size of that dis- Petters theof and model the results Kreidenweis CCN in data (2007). Fig. are It 5a from are various annual field mean campaigns values, that while lasted most a few weeks to months. locations, multiple CCN data are available for di oti et al., 2009; Hudsonas and Noble, well 2009; as Komppula et archival al., 2009; observations Kivekas (DOE’s et al., Atmospheric 2009), System Research). In some secondary CCN istem necessary. described Toward above this aredistribution presented end, of in the simulations Fig. annual with mean 5.persaturation the number of concentrations Figure 0.4 modeling % of 5a – sys- CCN shows CCN0.4)([CCN0.4] (defined averaged the at within a simulated the water spatial lower su- at boundary 26 layer (0–0.4 stations km) aroundletters the in globe. Fig. The 5a. locationsadditional of data The points the CCN0.4 taken 26 from data stations recent include publications are those (Dal indicated Mason compiled by et by the al., Andreae 2008; Bougiati- (2009), with duce total particle numberthis concentrations. mechanism, the However, e to assessnot only the on climatic nucleation rates, impact butsources of also of on primary the particles growth that ratesthat of can is nucleated act capable particles as of and cloud the reproducing nuclei. baseline In this ambient regard, distributions a of global observed model primary and and CN concentrations can begether, built the upon feedback specific e physicalpositive-CLAW) would laws imply (Figs. an 1–3). even more Takentrations to- significant of decrease tropospheric in particles the associated number with concen- future climate change. 3 E It is shown above that global warming could slow down new particle formation and re- long-term decreasing trends in CN concentrationscan at be remote explained sites by seem the to direct(Fig. exceed e what 3c) suggests that DMSing emissions (i.e., might supporting indeed be thefeedback decreasing mechanism positive-CLAW with hypothesis). linking global warm- temperature Compared increases to to CLAW, reductions a in positive nucleation rates “positive-CLAW”) (e.g., Kloster et al., 2007; Ayers and Cainey, 2007). The fact that the 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ers ff ecting ff ects on ff erent regions ff ects of warming on ff per degree of global warming, enough 2 − ect of clouds that form on the CCN, thus ect of increasing temperatures on new par- ff ff 27924 27923 1–3 % per degree of warming through most of over South America and India. The zonally av- 1 ∼ − ects on nucleation rates. Figure 6b suggests that C ff 1–0.3 W m ◦ and CN10, which show a maximum decrease in the . 0 J + ). 1 − C ◦ has large values in regions where particle growth rates are normally 4 . . Accordingly, the positive feedback process discussed here might con- 2 CCN0 − ) in the lower troposphere (0–2 km), and the scaled total CCN0.4 change pro- 1 − 1 W m C . ◦ This analysis only considers the direct e Global climate models project significant greenhouse warming by the end of this 0 nucleation rates and other factors (especially DMS emissions) would imply a significant action rates, oxidant concentrations, gas-particlemate model, partitioning, with and relevant key so processesimportance reasonably on. validated, of is A the needed global to positive cli- assessof feedback the these proposed other factors. in Nevertheless, thistions, the study which long-term have decreasing amid been trends associated observed in changes at1970s CN four concentra- and NOAA two ESRL/GMD baseline other stationsthe sites since specific since the positive feedback middle mechanism 1990s,port outlined appear in the this to idea study, not but thatCN also only feedback indirectly are, be sup- overall, processes consistent positive associated in with with nature. Taken other together, related the feedback factors e a tribute an additional forcing of to increase the sensitivity of Earth’s climate system significantly. ticle formation, ignoringemissions other of related aerosol factors precursor that compounds, the might hydrological play cycle, a photochemical re- role; e.g., e increases the mean sizetation, of and cloud decreases droplets, cloudalbedo reduces cover and cloud (Twomey, 1977; albedo, area, enhanceswarming IPCC, more – precipi- 2007). that solar is, energy By aCCN positive reducing reaches concentrations feedback of cloud occurs. the only It a surface,tions should few percent intensifying be (Platnick can pointed and the have out Twomey, substantial 1994). that initiating that climate changes forcing A a in implica- rough 1 calculation %∼ (Luo decrease and in Yu, 2011) CCN suggests may lead to a positive first-indirect radiative forcing of duced aerosol indirect radiativeamplifying the cooling initial e warming. Theof feedback new mechanism aerosol works particles, because aas the fraction ambient formation of which temperatures eventually rise. evolvecrophysical into It and CCN, is has optical suppressed long properties been in known such that CCN a influence way cloud that mi- a decrease in CCN generally 1–3 % CCN decrease for each degree Centigrade of regional warming leads to a re- that involves a decrease in cloudwith condensation nuclei regional (CCN) concentrations climate associated warming caused by greenhouse gas emissions. The estimated in CCN0.4 of upcrease in to CCN0.4 5–10 is % greatestincreases over in in many the temperatures, Arctic continental and/or andpredicted landmasses. high over for major sensitivity these continents regions. The of because CCN0.4 estimated of to large de- temperature change, 4 Summary and discussion The present analysis points to the existence of a positive climate feedback mechanism an example of howas such a warming result might impact ofthe CCN direct abundances impact temperature in e of di casts, global IPCC, warming 2007) based (according onno to the other positive NCAR specified feedback mechanism changes CCSM3 quantified in Scenario above emissions, (with SR-A1B or fore- other factors) could result in a decrease the troposphere. In contrasttropics, to PC fast (associated with anthropogenic anddecrease biogenic precursor in emissions). CCN0.4 exceeds The 3 maximum eraged % (not shown) decreases in(reaching CCN0.4 above are 2 % largest in the tropical lower troposphere century, with large uncertainties and spatial variations (IPCC, 2007). Figure 6b o (% jected to the end ofpredictions the corresponding century to the (2080–2099) IPCC-DDC based 4thFigure on Assessment Report NCAR 6a Scenario CCSM3 indicates SR-A1B. global that, warming CCN0.4 as concentrations the decrease result of by a decrease in nucleation rates, annual mean Figure 6 shows the changes in [CCN0.4] associated with increasing temperatures 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , O aerosols, At- 2 H hydrolysis on global − cient thermodynamic 5 − ffi O Cl 2 − − 3 , 2007. NO doi:10.5194/acp-6-3181-2006 doi:10.5194/acp-8-2405-2008 − − 2 4 SO − + Na − ect the climate sensitivity of greenhouse + ff NH4 27926 27925 . − + 2 , 2005. erson (NOAA/ESRL/GMD and U. Colo/CIRES) for the , 2009. ff doi:10.5194/acp-7-4639-2007 Mg − + 2 , 2009. , 2007. Ca and Aerosol Group of NOAA/ESRL’s GMD for making CN mea- − + ff This study is supported by NSF under grant AGS-0942106 and NASA un- doi:10.1029/2005GL022469 doi:10.5194/acp-9-7053-2009 vs. low-yield pathways, Atmos. Chem. Phys., 8, 2405–2420, 233–251, 1995. of global terrestrial isopreneAerosols from emissions Nature), using Atmos. MEGAN2006. Chem. (Model Phys., of 6, Emissions 3181–3210, of Gases and Global modeling of secondary organic aerosol formation from aromatic2008. hydrocarbons: high- equilibrium model formos. Chem. K Phys., 7, 4639–4659, Environ. Chem., 4, 366–374, 2007. and Schultz, M.:model Global description modeling and of evaluation, tropospheric J. Geophys. chemistry Res., with 106, 23073–23096, assimilated 2001. meteorology: atmospheric sulphur, cloud albedo and climate, Nature, 326, 655–661,nen, 1987. Y., Hansson, H.-C., andaerosol Kulmala, particle M.: number and Annual volumeTellus concentration and B, and interannual 60, their variation 495–508, connection in 2008. to particle boreal formation, forest model budgets of troposphericL09813, nitrogen oxides, ozone, and OH, Geophys. Res. Lett., 32, doi:10.5194/acp-9-543-2009 and Trautmann,from N. energy M.:doi:10.1029/2006GB002840 related combustion, Historical 1850–2000, emissions Global ofCloud Biogeochem. condensation black Cy., nucleiMediterranean: and measurements 21, CCN in organic closure GB2018, 7066, the and carbon marine droplet growth boundary aerosol kinetics, layer Atmos. of Chem. the Phys., 9, Eastern 7053– optical thickness in remote and polluted regions, Atmos. Chem. Phys., 9, 543–556, 10768, 1983. Hudson, J. G. and Noble, S.: CCN and cloud droplet concentrations at a remote ocean site, Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron,Henze, D. C.: K., Seinfeld, Estimates J. H., Ng, N. L., Kroll, J. H., Fu, T.-M., Jacob, D. J., and Heald, C. L.: Gras, J. L.: CN, CCN and particle size in Southern Ocean air at Cape Grim, Atmos. Res., 35, Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally e Evans, M. J. and Jacob, D. J.: Impact of new laboratory studies of N Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J., Field, B., Fiore, A., Li, Q., Liu, H., Mickley, L., Dal Maso, M., A. Hyvarinen, M. Komppula, Tunved, P., Kerminen, V.-M., Lihavainen, H., Viisa- Ayers, G. P. and Cainey, J. M.: The CLAW hypothesis: a review of the major developments, Charlson, R. J., Lovelock, J. E., Andreae, M. O., and Warren, S. G.: Oceanic phytoplankton, Bond, T. C., Bhardwaj, E, Dong, R., Jogani, R.,Bougiatioti, A., Jung, Fountoukis, C., S., Kalivitis, Roden, N., Pandis, C., S. N., Streets, Nenes, D. A., and G., Mihalopoulos, N.: Andreae, M. O.: Correlation between cloud condensation nuclei concentration and aerosol References Bodhaine, B.: Aerosol measurements at four background sites, J. Geophys. Res., 88, 10753– CCN data observed throughsored the by Atmospheric the Radiation USincreases; Measurement and Department (ARM) Hatakka Program of Juha spon- Energy;Database of the for the Finnish particle NCAR Meteorological size CCSM3model Institute distributions and is team the at managed for CREATE by Pallas projected Aerosol support the used temperature from Atmospheric in NASA’s Chemistry Atmospheric the Chemistry Modeling Supplement. Modeling Group and at The Analysis Harvard GEOS-Chem Program. University with der grant NNX11AQ72G. Wework: thank the the following Observatory sourcessurements of Sta available; information Rolf and Weller of data thefor used Alfred the in Wegener Institute this Neumayer for CN Polar and data; Marine Research Anne Je Acknowledgements. http://www.atmos-chem-phys-discuss.net/11/27913/2011/ acpd-11-27913-2011-supplement.pdf change. These feedbackgas processes emissions will and a shouldwarming. be incorporated in ongoing model assessmentsSupplementary material of related global to this article is available online at: decrease in the concentrations of tropospheric particles associated with future climate 5 5 30 25 20 25 15 20 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Be on wet 7 , 2008. , 2007. ¨ anen, P., Lehti- doi:10.5194/acp- , 2009. ¨ ais Pb and doi:10.5194/acp-10- , 2004. 210 , 2009. , 2009. , 2007. , 2008. , 2009. , 2007. ¨ ects of aerosols, J. Geophys. Res., arvenoja, S., R ff ¨ arinen, A., Komppula, M., Viisanen, Y., , 2010. ¨ arvinen, H., Lohmann, U., Bennartz, R., doi:10.5194/acp-8-3149-2008 , 2007. doi:10.5194/acp-7-1367-2007 doi:10.5194/acp-9-5461-2009 doi:10.1029/2003JD004473 O vapors, J. Geophys. Res., 107, 4381, 27928 27927 2 , 2003. H doi:10.1029/2008JD011007 − doi:10.1029/2006JG000224 doi:10.5194/acp-9-1747-2009 3 ¨ as, N., Komppula, M., Mihalopoulos, N., Kouvarakis, G., doi:10.1029/2007GL033038 doi:10.1029/2009GL038465 NH ¨ doi:10.1029/2006JD007813 aki, H., and Kulmala, M.: Parametrization of ternary ¨ arinen, A.-P., Kerminen, V.-M., Panwar, T. S., Sharma, V. P., ciency of cloud condensation nuclei formation from ultrafine − ¨ aki, H., Anttila, T., and Kulmala, M.: New parameterization of 4 ffi , 2007. , 2002. SO 2 doi:10.1029/2006JD007977 doi:10.5194/acp-10-10733-2010 , 2011. doi:10.1029/2002JD002622 , 2010. ¨ as, N., Sun, J., Zhan, M., Kerminen, V.-M., Hyv microphysics simulation withcomparison, assimilated Atmos. meteorology: Chem. Phys., 8, model 3149–3168, description and intermodel Merikanto, J., Mann, G. W., Chipperfield,H.: M. P.,Wiedensohler, A., Contribution Birmili, W., of andGeophys. Lihavainen, Res. particle Lett., formation 35, L06808, to global cloud condensationing, S., nuclei Ogren, concentrations, J. A.,Laakso, Andrews, L., E., Lihavainen, Baltensperger, U., H.,Jennings, Weingartner, S. E., Kivek G., Boy, O’Dowd, M., C., Kulmala,gri, Birmili, M., K., W., Bonn, Wiedensohler, B., A., Krejci, Weller, R., R.,and Laaksonen, Gras, A., Sun, J., Hamed, Laj, J.: A., P., Selle- Minikin,emissions Explaining A., and Harrison, global R. particle M., surface formation, Talbot, R., Atmos. aerosol4775-2010 Chem. number Phys., 10, concentrations 4775–4793, in terms of primary droplet concentration with the advanced33, very 334–347, 1994. high resolution radiometer, J. Appl. Meteorol., creases in tropospheric oxidants from photochemical e pollution influences on sulfate-nitrate-ammonium aerosolsfor in policy, J. the Geophys. United Res., States: 109, implications D15204, growth and clouddoi:10.5194/acp-7-1961-2007 condensation nucleus activity, Atmos.particles, Chem. Atmos. Chem. Phys., Phys., 7, 1367–1379, 7, 1961–1971, nen, K. E.Feichter, J., J., Laaksonen, and A., Kulmala,nucleation M.: Kerminen, and V.-M., Sensitivity J of secondary aerosolAtmos. organic Chem. concentrations distribution Phys., and 9, in cloud 1747–1766, properties ECHAM5-HAM to global circulation model, nucleation ratesdoi:10.1029/2002JD002132 for H aerosol over the United States:J. Geophys. comparison Res., of 112, climatological D06201, simulations with observations, deposition and transportingassimilated a meteorological global fields, J. threee-dimensional Geophys. Res., chemical 106, tracer 12109–12128, 2001. modelsulfate driven emission parameterizations, by Atmos. Chem.11-1949-2011 Phys., 11, 1949–1959, and Viisanen, Y.: Physical propertiesIndia, of J. Geophys. aerosol Res., particles 114, at D12202, a Himalayan background site in sulfuric acid-ammonia-water ternary nucleation ratesRes., at 112, tropospheric D15207, conditions, J. Geophys. Esch, M.: Response of dimethylsulfideing, (DMS) J. in Geophys. the Res., 112, ocean and G03005, atmosphere to global warm- 108, 4097, Hong, N., Zhang, Y.,particle Kulmala, M., size Zhang, distribution X.-C.,China, measurements Deli-Geer, Atmos. and Chem. at Phys., Lihavainen, 9, Mount H.: 5461–5474, Waliguan, Long a term high-altitude site in inland rachat, S., Lohmann,Earth’s U., radiative and forcing Feichter, in J.: the10, 10733–10752, aerosol-climate Aerosol model ECHAM5-HAM, nucleation Atmos. and Chem. its Phys., role for clouds and Geophys. Res. Lett., 36, L13812, bridge Univ. Press, Cambridge, United Kingdom and New York, NY, USA, 2007. Trivitayanurak, W., Adams, P. J., Spracklen, D. V., and Carslaw, K. S.: Tropospheric aerosol Spracklen, D. V., Carslaw, K. S., Kulmala, M., Kerminen, V.-M.,Spracklen, Sihto, D. S.-L., V., Riipinen, Carslaw, I., K. S., Merikanto, J., Mann, G. W., Reddington, C. L., Picker- Platnick, S. and Twomey, S.: Determining the susceptibility of cloud albedo to changes in Martin, R. V., Jacob, D. J., Yantosca, R. M., Chin, M., and Ginoux, P.: Global and regional de- Pierce, J. R. and Adams, P. J.: E Park, R. J., Jacob, D. J., Field, B. D., Yantosca, R. M., and Chin, M.: Natural and transboundary Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic Makkonen, R., Asmi, A., Korhonen, H., Kokkola, H., J Napari, I., Noppel, M., Vehkam Luo, G. and Yu, F.: Sensitivity of global cloud condensation nuclei concentrations to primary Liao H., Henze, D. K., Seinfeld, J. H., Wu, S., and Mickley, L.Liu, J.: H., Biogenic Jacob, secondary D. organic J., Bey, I., and Yantosca, R. M.: Constraints from Merikanto, J., Napari, I., Vehkam Komppula, M., Lihavainen, H., Hyv Kloster, S., Six, K. D., Feichter, J., Maier-Reimer, E., Roeckner, E., Wetzel, P., Stier, P., and Kivek Kazil, J., Stier, P., Zhang, K., Quaas, J., Kinne, S., O’Donnell, D., Rast, S., Esch, M., Fer- IPCC: Climate Change 2007: The Physical Science Basis, edited by: S. Solomon, et al., Cam- 5 5 30 20 25 15 30 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , nucle- 3 NH − , 2010. O 2 H − ciency of boundary 4 ffi SO O homogeneous nucle- 2 2 H − , 2009. 4 doi:10.5194/acp-10-7545-2010 SO 2 , 2011. , 2011. doi:10.1029/2009JD012630 , 2009. , 2010. 27930 27929 doi:10.5194/acp-9-239-2009 , 2009. , 2006. , 2002. doi:10.5194/acp-11-1083-2011 doi:10.5194/acp-11-9451-2011 doi:10.1029/2009JD013473 doi:10.5194/acp-9-7691-2009 ect of ammonia on new particle formation: a kinetic H ff ¨ aki, H., Kulmala, M., Napari, I., Lehtinen, K. E. J., Timmreck, C., Noppel, ation, J. Chem. Phys., 127, 054301, 2007. and look-up table, J. Geophy. Res., 115, D03206, and kinetic condensation ofPhys., 11, organic 1083–1099, compounds: global scale implications, Atmos. Chem. 7691–7710, nostic of competitive nucleationPhys., mechanisms 11, under 9451–9463, atmospheric conditions, Atmos. Chem. Wu, S.: Spatialsimulations, distributions observations, of and particle implications number for concentrations nucleation in mechanisms, the J. Geophys. global Res., troposphere: 115, D17205, doi:10.5194/acp-9-5131-2009 2010. ation model constraineddoi:10.1029/2005JD005968 by laboratory measurements, J. Geophys. Res., 111, D01204, tribution of nucleation to aerosol and CCN number concentrations, Atmos. Chem. Phys., 9, Park, I. S., Reddy, S., Fu,emissions J. in S., Chen, 2006 D., Duan, for L., Lei, the Y., Wang, NASA L. T., INTEX-B and Yao, mission, Z. L.: Atmos. Asian Chem. Phys., 9, 5131–5153, Boucher, O.: Lowof sensitivity dimethyl-sulphide, Atmos. of Chem. cloud Phys., 10, condensation 7545–7559, nuclei to changes in the sea-air flux M., andation Laaksonen, rates A.: fordoi:10.1029/2002JD002184 tropospheric An and stratospheric improved conditions, parameterization J.Atmos. for Chem. Geophys. Phys., sulfuric Res., 9, 239–260, acid/water 107, 4622, nucle- layer aerosol at coastal Neumayer Station, Antarctica. J. Geophys. Res., 113, D16208, 2008. 1149–1152, 1977. Yu, F.: Ion-mediated nucleation in the atmosphere: key controlling parameters, implications, Yu, F.: Improved quasi-unary nucleation model for binary H Yu, F.: A secondary organic aerosol formation model considering successive oxidation aging Yu, F., Luo, G., Bates, T., Anderson, B., Clarke, A., Kapustin, V., Yantosca, R., Wang, Y., and Yu, F. and Luo, G.: Simulation of particle size distributionYu, with F. and a Turco, R. global P.: The aerosol size-dependent charge model: fraction of con- sub-3-nm particles as a key diag- Zhang, Q., Streets, D. G., Carmichael, G. R., He, K. B., Huo, H., Kannari, A., Klimont, Z., Yu, F.: E Woodhouse, M. T., Carslaw, K. S., Mann, G. W., Vallina, S. M., Vogt, M., Halloran, P. R., and Weller, R. and Lampert, A.: Optical properties and sulfate scattering e Vehkam Wang, M. and Penner, J. E.: Aerosol indirect forcing in a global model with particle nucleation, Twomey, S.: The influence of pollution on the shortwave albedo of clouds, J. Atmos. Sci., 34, 5 5 20 30 25 15 10

], in cm (c) 4 ). IMN rates 3 SO − 2 is also given for 1 cm

− 10 ppt; 2 s = 3 ] Kinetic THN model of − 3 50 %). The percentage = (d) , in µm 5 cm . S 0 = ) (RH T J 100 ppt; at = ] T classical BHN model of Vehkamaki 3 ). BHN is an integral part of IMN, and 1 − (a) s is also given for each case. 3 1 O binary homogeneous nucleation (BHN) − 2 − s ) constrained by experimental results. In the H 3 3

. The percentage change (PC) in the nucleation − 2 1 − 3 4 NH − 27932 27931 C increase of ◦ SO cm 5 cm 2 . 8 0 10 = ) per × J J 0 ) on H . , in ion-pairs cm at T 1 Q T = ] 4 SO erent nucleation models: at low temperatures when BHN is dominant. 2 ﬀ ternary homogeneous nucleation (THN) under a range of atmospheric 3 BHN J NH classical THN model of Napari et al. (2002) with [NH −

O 2 (b) H ect of temperature ( − ﬀ 4 E Dependence of ion-mediated nucleation (IMN) rates (solid lines) and binary homoge-

Figure 2. Figure SO ) per degree increase in 2 J approaches to

IMN Yu (2006) with ammonia stabilizinglegend, factor read A1E8 (F as [H rate ( and H conditions based on di et al. (2002); classical THN model of Merikanto et al. (2007) with [NH Fig. 2. Fig. 1. neous nucleation (BHN) rates (dashed lines) on temperature ( J also depend on ionization rate ( change (PC) in the nucleationeach rate case. ( Lines of same colorand have the same sulfuric same acid local vapor surface concentration ([H area density of pre-existing particles ( Create PDF files without this message by purchasing novaPDF printer (http://www.novapdf.com)

. . 3 4 27934 27933

. . , the locations of four NOAA baseline stations (BRW, MLO, SMO, and SPO), one (c) Horizontal (i.e., averaged within the 0–2 km surface layer) and vertical (i.e., zonally Annual mean CN values observed at the ﬁve NOAA sites and one German Antarc-

Figure 3 Figure

4 Figure ) and concentrations of condensation nuclei larger than 10 nm (CN10), for a uniform 1 J Fig. 4. tic station identiﬁedobservations, in with Fig. mean values 3c. and slopes In provided in the the ﬁgure, legend. linear regressions (LR) are shown for the CN Fig. 3. averaged) spatial distributions( of the annual mean percentage changes in nucleation rates NOAA regional station (BND), and oneas German A, Antarctic station B, (NEU) C, are D, marked E, respectively and F. The location of Pallas station discussed in Supplement is marked as G. temperature increase. These temperature sensitivitiesIn were calculated panel as described in the text. files without this message by purchasing novaPDF printer (http://www.novapdf.com) Create PDF files without this message by purchasing novaPDF printer Create PDF files without this message by purchasing novaPDF printer (http://www.novapdf.com) Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (a)

, the site identiﬁer and (b) Comparison of simulated C) in the lower troposphere (b) ◦ ). LBL 6 5 27936 27935

. , model results correspond to the months of the observations, and vertical 6

(b) with observed values at 26 sites around the globe (locations are indicated in Simulated global distribution of CCN concentrations (annual mean number con- Figure Figure LBL Sensitivity of CCN0.4 to temperature change (% per

5. Figure Create PDF files without this message by purchasing novaPDF printer (http://www.novapdf.com) (0–2 km), and the scaledperiod) total based CCN0.4 on NCAR change CCSM3 (%) projectedDDC global toward 4th temperature the Assessment changes end corresponding Report of to Scenario IPCC- this SR-A1B. century (2080–2099 Fig. 6. [CCN0.4] by letters). In bars indicating variance are color coded for clarity. bars deﬁne the simulated ranges of monthly mean CCN0.4 values. In Fig. 5. (a) centration for CCN thatlower boundary are layer active (0–0.4 at km) a (designated as water [CCN0.4] supersaturation of 0.4 %) averaged within the Create PDF files without this message by purchasing novaPDF printer (http://www.novapdf.com)