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Constraining the Instantaneous Aerosol Influence on Cloud Albedo

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Constraining the Instantaneous Aerosol Influence on Cloud Albedo Constraining the instantaneous aerosol influence on cloud albedo Edward Gryspeerdta,b,1, Johannes Quaasa, Sylvaine Ferrachatc, Andrew Gettelmand, Steven Ghane, Ulrike Lohmannc, Hugh Morrisond, David Neubauerc, Daniel G. Partridgef,g,h, Philip Stieri, Toshihiko Takemuraj, Hailong Wange, Minghuai Wange,k,l,m, and Kai Zhange aInstitute for Meteorology, Universitat¨ Leipzig, 04109 Leipzig, Germany; bSpace and Atmospheric Physics Group, Imperial College London, London SW7 2AZ, United Kingdom; cInstitute for Atmospheric and Climate Science, ETH Zurich, 8092 Zurich, Switzerland; dNational Center for Atmospheric Research, Boulder, CO 80305; eAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA 99352; fDepartment of Environmental Science and Analytical Chemistry, Stockholm University, SE-106 91 Stockholm, Sweden; gBert Bolin Centre for Climate Research, Stockholm University, SE-106 91 Stockholm, Sweden; hDepartment of Mathematics, University of Exeter, Exeter EX4 4QF, United Kingdom; iAtmospheric, Oceanic, and Planetary Physics, Department of Physics, University of Oxford, Oxford OX1 3PU, United Kingdom; jResearch Institute for Applied Mathematics, Kyushu University, Fukuoka 816-8580, Japan; kInstitute for Climate and Global Change Research, Nanjing University, 210023 Nanjing, China; lSchool of Atmospheric Sciences, Nanjing University, 210023 Nanjing, China; and mCollaborative Innovation Center of Climate Change, 210023 Nanjing, China Edited by John H. Seinfeld, California Institute of Technology, Pasadena, CA, and approved March 17, 2017 (received for review November 4, 2016) Much of the uncertainty in estimates of the anthropogenic forcing day (PD), when combined with an estimate of the correspond- of climate change comes from uncertainties in the instantaneous ing anthropogenic change in AOD (such as ref. 13). Two main effect of aerosols on cloud albedo, known as the Twomey effect assumptions are made in this process: that the AOD is a suitable or the radiative forcing from aerosol–cloud interactions (RFaci), proxy of the cloud condensation nuclei (CCN) concentration at a component of the total or effective radiative forcing. Because the cloud base and that the relationships between aerosol and aerosols serving as cloud condensation nuclei can have a strong the Nd in the PD (determined by spatiotemporal variability) are influence on the cloud droplet number concentration (Nd ), previ- indicative of the actual sensitivity of cloud properties to aerosol ous studies have used the sensitivity of the Nd to aerosol proper- perturbations. ties as a constraint on the strength of the RFaci. However, recent Recent work has called both of these assumptions into ques- studies have suggested that relationships between aerosol and tion. Observational (14) and model-based (15) studies have cloud properties in the present-day climate may not be suitable shown a disconnect between AOD and CCN. Because the AOD for determining the sensitivity of the Nd to anthropogenic aerosol is a column-integrated measurement, it does not provide vertical perturbations. Using an ensemble of global aerosol–climate mod- information about the location of the aerosol. It also lacks infor- els, this study demonstrates how joint histograms between Nd mation about the composition of the particles and is weighted and aerosol properties can account for many of the issues raised preferentially toward larger particles (4), missing information by previous studies. It shows that if the anthropogenic contribu- about smaller aerosol particles that are often emitted from tion to the aerosol is known, the RFaci can be diagnosed to within anthropogenic activities (16). 20% of its actual value. The accuracy of different aerosol proxies Second, it has been shown that the PD AOD–Nd relationship for diagnosing the RFaci is investigated, confirming that using the may not be representative of the true strength of the interac- aerosol optical depth significantly underestimates the strength of tion between aerosols and cloud properties due to the differing the aerosol–cloud interactions in satellite data. PI and PD aerosol environments (17). Additionally, it has been aerosols j clouds j radiative forcing Significance Uncertainties in the strength of aerosol–cloud interactions he radiative forcing due to anthropogenic aerosols is the drive the current uncertainty in the anthropogenic forcing most uncertain component of the anthropogenic radiative T of the climate. Previous studies have highlighted shortcom- forcing (1), with the interaction between aerosols and clouds ings in using satellite data for determining the forcing, which generating much of this uncertainty. As cloud droplets form on underestimate the strength of the aerosol forcing. This work aerosol particles, changes in the aerosol number concentration demonstrates that the component of the radiative forcing can change the cloud droplet number concentration (N ), gener- d from aerosol–cloud interactions due to the instantaneous ating an instantaneous radiative forcing by increasing the cloud effect on cloud reflectivity (RFaci) can be calculated to within brightness, known as the Twomey effect (2) or radiative forcing 20%, using only present-day observations of the variabil- from aerosol–cloud interactions (RFaci) (1) (referring only to EARTH, ATMOSPHERIC, ity of aerosol and cloud properties, provided the anthro- AND PLANETARY SCIENCES liquid clouds in this work). Together with other changes in cloud pogenic component of the aerosol is known. The model results properties due to changes in N (e.g., ref. 3), the RFaci is a com- d are combined with satellite data to provide an improved ponent of the total or effective radiative forcing (ERFaci). observations-based estimate of the RFaci, paving the way for Due to the sparse nature of preindustrial (PI) observations of more accurate estimates of the aerosol influence on climate. cloud properties, the influence of aerosols on cloud properties is often inferred from observations of the present-day spatiotem- Author contributions: E.G. designed research; E.G., S.F., A.G., S.G., U.L., H.M., D.N., D.G.P., poral variability of aerosol and cloud properties (e.g., refs. 4–7). P.S., T.T., H.W., M.W., and K.Z. performed research; S.F., A.G., S.G., U.L., H.M., D.N., D.G.P., Although much of the variation between aerosol and cloud prop- P.S., T.T., H.W., M.W., and K.Z. contributed new reagents/analytic tools; E.G. analyzed erties can be attributed to variations of meteorological factors data; and E.G. and J.Q. wrote the paper. (e.g., refs. 8 and 9), the sensitivity of Nd to aerosol optical depth The authors declare no conflict of interest. (AOD) is thought to be largely independent of these factors. It This article is a PNAS Direct Submission. is therefore often used in observational estimates of the strength Data deposition: The model history is available in the AeroCom archive at aerocom. of aerosol–cloud interactions (7, 10, 11). This sensitivity (5) has met.no/data.html. been shown to be a useful “emergent constraint” on the strength 1To whom correspondence should be addressed. Email: [email protected]. of the ERFaci in general circulation models (12), providing a This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. method to calculate the change in Nd from the PI to the present 1073/pnas.1617765114/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1617765114 PNAS j May 9, 2017 j vol. 114 j no. 19 j 4899–4904 Downloaded by guest on September 27, 2021 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 pollution (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.
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