Downloaded by guest on September 27, 2021 fteEFc ngnrlcruainmdl 1) rvdn a providing (12), models in change circulation the calculate general to strength in method the on ERFaci constraint” has the “emergent (5) useful of sensitivity a be This to 11). shown 10, been strength (7, It the interactions of factors. estimates aerosol– these observational of of in independent used often largely therefore be is to factors of thought sensitivity meteorological is the of (AOD) 9), and variations 8 to refs. attributed (e.g., prop- be cloud and can 4–7). aerosol erties refs. between variation (e.g., the properties of cloud spatiotem- much Although and present-day aerosol the of of variability is poral observations properties from cloud on inferred aerosols often of influence the properties, cloud (ERFaci). forcing radiative effective or total the of cloud ponent in in changes changes other to to with due only Together properties work). (referring this (1) in forcing radiative (RFaci) liquid or interactions (2) aerosol–cloud effect cloud Twomey from the the increasing as by known forcing brightness, radiative instantaneous (N an concentration concentration ating number number droplet cloud aerosol on the the form clouds change can droplets in and cloud changes aerosols As particles, uncertainty. between aerosol this interaction of much the generating with (1), forcing T aerosols data. satellite in of interactions strength aerosol–cloud the the underestimates the significantly using proxies depth that aerosol optical confirming different aerosol investigated, is of RFaci accuracy the within The diagnosing to value. for diagnosed actual be can its RFaci contribu- of the anthropogenic 20% known, the is raised aerosol if the issues that to the shows tion of It many studies. between for previous histograms account by can joint properties how aerosol demonstrates and study mod- aerosol– this global els, of suitable ensemble an be Using not perturbations. the may of sensitivity climate the and present-day determining aerosol for the between recent in However, relationships properties RFaci. that cloud the of suggested strength have the studies on constraint the a of as sensitivity ties the (N used strong concentration have a number studies have droplet ous cloud can Because the nuclei forcing. on condensation radiative influence cloud effective as or (RFaci), serving total interactions aerosols the aerosol–cloud of effect Twomey from component the forcing a as radiative known the , cloud instantaneous or on the aerosols in 2016) of uncertainties 4, November from effect review comes forcing for change anthropogenic (received the climate 2017 of 17, of estimates March in approved uncertainty and the CA, of Pasadena, Much Technology, of Institute California Seinfeld, H. John by Edited and China; Kingdom; Nanjing, United 210023 University, 3PU, Nanjing OX1 Sciences, Oxford Oxford, Japan; of 816-8580, University Fukuoka University, Physics, of Department Sweden; Physics, Stockholm, Planetary Sweden; 91 Stockholm, SE-106 91 University, SE-106 Stockholm University, Chemistry, Analytical and Science Environmental 80305; CO Boulder, Kingdom; United 2AZ, a Wang Minghuai www.pnas.org/cgi/doi/10.1073/pnas.1617765114 Morrison Hugh Gryspeerdt Edward albedo on cloud influence aerosol instantaneous the Constraining nttt o eerlg,Universit Meteorology, for Institute u otesas aueo ridsra P)osrain of observations (PI) preindustrial of nature sparse the to Due erdaiefrigdet nhooei eoosi the radiative is anthropogenic the aerosols of anthropogenic component to uncertain due most forcing radiative he | clouds | aitv forcing radiative e d topei cecsadGoa hneDvso,Pcfi otws ainlLbrtr,Rcln,W 99352; WA Richland, Laboratory, National Northwest Pacific Division, Change Global and Sciences Atmospheric e,k,l,m ai Neubauer David , c nttt o topei n lmt cec,EHZrc,89 uih Switzerland; Zurich, 8092 Zurich, ETH Science, Climate and Atmospheric for Institute a,b,1 n a Zhang Kai and , oansQuaas Johannes , N d tLizg 40 epi,Germany; Leipzig, 04109 Leipzig, at ¨ k eg,rf ) h Fc sacom- a is RFaci the 3), ref. (e.g., nttt o lmt n lblCag eerh ajn nvriy 103Nnig China; Nanjing, 210023 University, Nanjing Research, Change Global and Climate for Institute N h d N eateto ahmtc,Uiest fEee,Eee X Q,Uie Kingdom; United 4QF, EX4 Exeter Exeter, of University Mathematics, of Department N c ailG Partridge G. Daniel , d rmteP otepresent the to PI the from d oatrpgncaerosol anthropogenic to e oarslotcldepth optical aerosol to N a d yvieFerrachat Sylvaine , oarslproper- aerosol to m olbrtv noainCne fCiaeCag,202 ajn,China Nanjing, 210023 Change, Climate of Center Innovation Collaborative d d ,gener- ), ,previ- ), f,g,h N b pc n topei hsc ru,Ipra olg odn odnSW7 London London, College Imperial Group, Physics Atmospheric and Space d hlpStier Philip , c nrwGettelman Andrew , 1073/pnas.1617765114/-/DCSupplemental IadP eoo niomns(7.Adtoal,i a been has it Additionally, interac- differing (17). the the environments to of aerosol due PD strength properties and true cloud PI and the aerosols of between representative tion be not may bu mle eoo atce htaeotneitdfrom emitted often are that particles (16). information activities missing anthropogenic weighted aerosol is (4), smaller and particles about particles larger the toward of infor- lacks preferentially composition also the It aerosol. about the mation of vertical location provide have the not about AOD does information studies the it Because measurement, (15) column-integrated CCN. a and is model-based AOD between and disconnect a shown (14) Observational tion. aerosol to properties cloud of sensitivity perturbations. and actual aerosol the between of indicative relationships the that at the and concentration base (CCN) suitable nuclei cloud a condensation main the is cloud AOD Two the the 13). that of ref. process: proxy this as correspond- in (such the made are AOD of assumptions in estimate change an anthropogenic with ing combined when (PD), day hsatcecnan uprigifrainoln at online information supporting contains article This at archive AeroCom the in 1 available is history model met.no/data.html. The deposition: Data Submission. Direct PNAS a is article This interest. of conflict analyzed no E.G. declare authors tools; paper.The the reagents/analytic wrote new J.Q. and contributed E.G. D.G.P., K.Z. and D.N., data; and H.M., U.L., M.W., S.G., H.W., A.G., S.F., T.T., research; P.S., performed K.Z. and M.W., H.W., T.T., P.S., uhrcnrbtos ..dsge eerh ...... D.G.P., D.N., H.M., U.L., S.G., A.G., S.F., E.G., research; designed E.G. contributions: Author owo orsodnesol eadesd mi:[email protected]. Email: addressed. be should correspondence whom To oeacrt siae ftearsliflec nclimate. improved on influence an aerosol for the provide way of the estimates to accurate paving more RFaci, data the anthro- of satellite estimate the observations-based with provided results combined model properties, The variabil- known. are cloud is the aerosol the and of of component pogenic aerosol observations of present-day within to ity instantaneous only calculated be using the can 20%, (RFaci) to forcing reflectivity radiative cloud due on the effect interactions of aerosol–cloud component work from This the forcing. that aerosol which the demonstrates forcing, of the strength determining shortcom- the for highlighted underestimate data forcing have satellite anthropogenic using studies in the Previous ings climate. in interactions the uncertainty aerosol–cloud of current of the strength drive the in Uncertainties Significance eod thsbe hw htteP AOD–N PD the that shown been has it Second, ques- into assumptions these of both called has work Recent N d nteP dtrie ysaitmoa aiblt)are variability) spatiotemporal by (determined PD the in i ohhk Takemura Toshihiko , j eerhIsiuefrApidMteais Kyushu Mathematics, Applied for Institute Research PNAS g etBlnCnr o lmt eerh Stockholm Research, Climate for Centre Bolin Bert d | tvnGhan Steven , a ,2017 9, May d ainlCne o topei Research, Atmospheric for Center National . j aln Wang Hailong , | o.114 vol. www.pnas.org/lookup/suppl/doi:10. e lieLohmann Ulrike , i topei,Oenc and Oceanic, Atmospheric, f l eatetof Department colo Atmospheric of School | o 19 no. e d , relationship | 4899–4904 aerocom. c ,

EARTH, ATMOSPHERIC, AND PLANETARY SCIENCES shown (18) that in many global aerosol–climate models the PD a saturation at higher AOD, where the Nd only weakly increases sensitivity of Nd to CCN variations (the slope of the linear regres- with increasing AOD. Others show a weak AOD–Nd relation- sion between Nd and CCN concentrations) is in many cases not ship at low AOD, followed by a stronger relationship as the AOD representative of the sensitivity of Nd to the anthropogenic per- increases (ECHAM6-HAM and SPRINTARS). The enforced turbation of CCN (the PD–PI change in Nd divided by the cor- lower bound to the Nd apparent in some simulations may be responding change in CCN evaluated from climate simulations). dNd responsible for the lower sensitivity of Nd to AOD ( dln(A) ) at This suggests that it would be challenging to constrain the mag- low AODs in these models (12), although low sensitivities at low nitude of the RFaci using only PD observations of the sensitivity AOD have also been observed in satellite data (11). of Nd to aerosol variations. All of the models show some difference in the AOD–Nd rela- In this work, techniques are presented to address these chal- tionship between the PD and the PI (Fig. 1, third column), mostly lenges. To account for nonlinearity in the aerosol–Nd relation- with higher Nd s for a given AOD in the PD simulation com- ship and the differing PI and PD aerosol environments, normal- pared with the PI. It is stronger at high AODs, suggesting that ized joint histograms are used to characterize the relationship this effect is due to the different composition of aerosols in (following ref. 11). A variety of different global aerosol–climate the PD compared with the PI. When the atmosphere is clean models that contributed to the AeroCom intercomparison (18, (low AOD), the aerosol composition is similar in the PI and the 19) are used to investigate the utility of different aerosol prox- PD simulations. However, high-AOD conditions occur mainly in ies for diagnosing the anthropogenic change in cloud-top Nd . dusty regions in the PI simulation (where the aerosol is a poor Together with joint histograms, this work investigates how accu- CCN), but in the PD simulation these high-AOD conditions are rately the RFaci could be diagnosed under ideal conditions, using often the result of anthropogenic (which on average is PD relationships between aerosol and cloud properties. a much better CCN). The situation is very different when using CCN at 1-km alti- Results tude and 0.3% supersaturation (CCN1km ) instead of the AOD Aerosol–Nd Relationships. Two-dimensional (“joint”) histograms as the parameter representing the aerosol (Fig. 1, fourth col- of Nd and aerosol properties are used in this work to account umn). The CCN1km –Nd relationships are still mostly nonlin- for the influence of nonlinearities in the relationship (11). Each ear, although there is less variation between the models than column of the joint histogram is normalized so that it sums to 1, for the AOD–Nd joint histograms. Importantly, the PD and such that it becomes an array of conditional probabilities. For PI CCN1km –Nd relationships are very similar, showing much example, the top left histogram in Fig. 1 shows the probabil- smaller differences in the joint histograms than are evident for ity of finding a specific Nd , given that a certain AOD has been the AOD–Nd relationship (Fig. 1, sixth column). At lower super- observed. saturations (0.1%) the CCN is weighted toward larger particles Joint histograms of cloud-top Nd versus an aerosol proxy for and the PD and PI relationships are not as close (Fig. S1). How- a selection of models from the AeroCom intercomparison (18, ever, the PD global CCN1km –Nd joint histogram is a reasonable 19) (gridded to 2.5◦ by 2.5◦) are shown in Fig. 1. Although there indicator of the PI relationship, as long as there is enough data is a general increase in cloud-top Nd with increasing AOD (Fig. at low CCN concentrations to properly create a joint histogram. 1, first and second columns), the nature of this increase varies It is also clear that the nonlinearity of these relationships significantly among the models. Some of the models (the CAM5 will influence any calculations made using a linear regression, variants) show a strong increase in Nd at lower AOD, followed by where the sensitivity would otherwise depend on the prevailing

Fig. 1. Joint histograms between aerosol properties (AOD and CCN1km, respectively, x axis) and cloud-top Nd (y axis) for each of the general circulation models (GCMs) used in this study. The first and second columns show the AOD-Nd joint histograms for the PD and the PI simulations, respectively. The histograms are normalized so each column sums to 1, such that the histograms show the probability of observing a specific cloud-top Nd, given a certain AOD (or CCN1km). The black line shows the mean Nd at each AOD and gray regions indicate missing data. The third column shows the difference between the PD and the PI relationships. The second set of three columns are the same as the first three but use CCN1km at 0.3% supersaturation instead of AOD as the independent variable.

4900 | www.pnas.org/cgi/doi/10.1073/pnas.1617765114 Gryspeerdt et al. 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EARTH, ATMOSPHERIC, AND PLANETARY SCIENCES A CCN1km when attempting to diagnose ∆Nd and the RFaci (Fig. 3C). Given this improved accuracy compared with using AOD as an aerosol proxy, AI and Nd data from the moderate-resolution imaging spectroradiometer (MODIS) are used to generate both regional joint histograms (Hist AI regional, 15◦ by 15◦ regions) and a single global joint histogram (Hist AI global), using 10 y of data (2004–2013). These are then combined with the annual mean MODIS liquid cloud fraction and the cloud susceptibil- ity derived from MODIS and Clouds and the Earth’s Radiant B Energy System (CERES) (Eq. 3) to provide an updated estimate of the RFaci (Fig. 4). Using the PI-to-PD AI changes from each of the models gives a range of RFaci estimates for the regional method between −0.18 and −0.58 Wm−2 and between −0.29 and −1.01 Wm−2 if using a single global AI–Nd joint histogram (Fig. S11). The RFaci is generally higher over the ocean due to the higher liq- C uid cloud fraction and cloud susceptibility, despite the smaller oceanic ∆Nd (Fig. 2). Although this is not a large selection of models, the mean value of −0.29 Wm−2 for the regional method and −0.49 Wm−2 for the global histogram are instructive to com- pare with the −0.2 Wm−2 mean value using a single global AOD- −2 Nd histogram (Fig. 4C), −0.2 Wm using local OLS with AOD (7) and −0.4 Wm−2 using local OLS and AI (21). There are some caveats to this estimate. First, the MODIS AI has little quantitative skill over land (22) and in some regions a positive RFaci is diagnosed from changes in the Nd (Fig. 4). This has a larger impact on the regional histogram method and may result in a reduction in the strength of the implied aerosol forc- ing. However, only a small fraction of the forcing comes from continental regions, similar to the findings from ref. 7, so this

Fig. 3. Comparison of different methods and proxies for calculating ∆Nd. may not result in a large bias in the global mean RFaci. Also, “Hist” indicates the use of a joint histogram and “OLS” the use of an ordi- the global histogram method is more likely to overestimate the nary least-squares regression. (A) The determination coefficient between RFaci (Fig. 3C), suggesting that the actual value is between the ◦ ◦ −2 the diagnosed and the actual values of the ∆Nd at a 2.5 by 2.5 resolution two estimates, perhaps around −0.4 Wm (this only includes globally. (B) The relative size of the global mean ∆Nd.(C) The relative size changes to cloud albedo and not other rapid cloud adjustments). of the implied global mean RFaci, with a percentage less than 100% indicat- It is also possible that systematic biases in the MODIS AI or N ing an underestimate in the estimated RFaci. The horizontal bars are at 80% d and 120%. The plots summarized in this figure are shown in Figs. S2–S10. retrieval could further affect this result, although the magnitude and sign of these effects is unclear. It should also be noted that this estimate is strongly dependent on the estimate of the anthro- Using regional AI–Nd joint histograms for diagnosing ∆Nd gives pogenic aerosol fraction. Because all of the AeroCom models in 2 r values between the diagnosed and the actual ∆Nd (0.61 to this work use the same emissions database, the diversity in the 0.81) approaching those of the CCN1km . Because the models do not provide the RFaci, the relative error in the RFaci is esti- mated by weighting ∆Nd by the observed liquid cloud fraction A and cloud albedo susceptibility (Fig. 3C, see Materials and Meth- ods for details). In general, the regional joint histograms pro- vide a more accurate diagnosis of RFaci, although using a sin- gle global histogram results in only a small increase in the error, even though the r2 value decreases for all of the models (Fig. 3A). The AOD performs worst as a parameter for characteriz- ing aerosol in the models when diagnosing ∆Nd and RFaci. The B local linear regressions have the lowest r2 values of all of the methods and proxies investigated, although the RFaci estimate when using AOD is slightly improved compared with the regional linear regression, possibly due to the reduced aerosol type vari- ability for a local regression (Fig. 3C). From these results, it is clear that estimates of the aerosol forcing that rely on the relationship between AOD and Nd for C characterizing the strength of aerosol–cloud interactions (such as many observational estimates) are likely to underestimate the anthropogenic perturbation of Nd by at least 30% (up to 90%). This would lead to an underestimate in the strength of the radia- tive forcing from aerosol indirect effects in these studies of at least 20% (up to 90%). Fig. 4. The mean of the individual model RFaci estimates (Fig. S11), using Satellite-Based Estimate. Although using the AOD as an aerosol MODIS data to create (A) regional histograms, (B) a single global AI–Nd his- proxy can lead to an underestimate when diagnosing the aerosol togram, and (C) a single global AOD–Nd histogram, combined with model forcing, the AI is almost as good a proxy for the aerosol as the estimates of the anthropogenic AI/AOD contribution.

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EARTH, ATMOSPHERIC, AND PLANETARY SCIENCES MODIS annual mean liquid cloud fraction (fliq) and the down-welling solar Research Council (ERC) under the European Union’s Seventh Framework Pro- flux (F↓) to produce a simple estimate of the RFaci (∆F↑) (28): gram (FP7/2007–2013)/ERC Grants FP7-306284 (“QUAERERE”), FP7-280025 (“ACCLAIM”), and FP7-603445 (“BACCHUS”); Natural Environment Research ↑ ↓ αcld(1 − αcld) ∆F = −F fliq ∆Nd. [3] Council Grant NE/I020148/1; Austrian Science Fund J 3402-N29 (Erwin 3Nd Schrodinger¨ Fellowship Abroad); Environment Research and Technology The MODIS AI is used to provide an observational constraint on the RFaci by Development Fund S-12-3 of the Ministry of the Environment, Japan; Japan Society for the Promotion of Science KAKENHI Grants JP15H01728 and generating AI–Nd joint histograms from observations. For these histograms, JP15K12190; National Natural Science Foundation of China Grants 41575073 the Nd is calculated using the adiabatic approximation, as specified in ref. and 41621005; Swiss National Supercomputing Center Project s431; and the 11. The AI is calculated from the AOD–Angstr˚ om¨ exponent joint histogram supercomputer system of the National Institute for Environmental Studies, in the MODIS MYD08 D3 product using only grid boxes where no ice cloud is Japan. The Pacific Northwest National Laboratory (PNNL) is operated for the Department of Energy (DOE) by Battelle Memorial Institute under Con- detected (to reduce possible cirrus contamination). Because the relative error tract DE-AC06-76RLO 1830. Work at PNNL was supported by the US DOE of the MODIS AOD and hence the Angstr˚ om¨ exponent and AI is large at low Decadal and Regional Climate Prediction using Earth System Models pro- AOD (<0.03), the Nd is assumed constant at AI values below 0.03. gram and by the US DOE Earth System Modeling program. The ECHAM6- HAM model was developed by a consortium composed of ETH Zurich, Max ACKNOWLEDGMENTS. We thank Helen Brindley (Imperial College London) Planck Institut fur¨ Meteorologie, Forschungszentrum Julich,¨ University of for her comments on the manuscript. The model data were provided Oxford, the Finnish Meteorological Institute, and the Leibniz Institute for through the AeroCom initiative. The MODIS data were provided by the Tropospheric Research and is managed by the Center for Climate Systems NASA Goddard Space Flight Center and the CERES data from the NASA Modeling (C2SM) at ETH Zurich, which also provided technical and scientific Langley Research Center. This work was supported by the European support.

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