Solar geoengineering has been proposed as a backup plan to offset some aspects of anthropogenic climate change if timely CO 2 emission reductions fail to materialize. Modeling studies have shown that there are trade‐offs between changes in temperature and hydrological cycle in response to solar geoengineering. Here we investigate the possibility of stabilizing both global mean temperature and precipitation simultaneously by combining two geoengineering approaches: stratospheric sulfate aerosol increase (SAI) that deflects sunlight to space and cirrus cloud thinning (CCT) that enables more longwave radiation to escape to space. Using the slab ocean configuration of National Center for Atmospheric Research Community Earth System Model, we simulate SAI by uniformly adding sulfate aerosol in the upper stratosphere and CCT by uniformly increasing cirrus cloud ice particle falling speed. Under an idealized warming scenario of abrupt quadrupling of atmospheric CO 2 , we show that by combining appropriate amounts of SAI and CCT geoengineering, global mean (or land mean) temperature and precipitation can be restored simultaneously to preindustrial levels. However, compared to SAI, cocktail geoengineering by mixing SAI and CCT does not markedly improve the overall similarity between geoengineered climate and preindustrial climate on regional scales. Some optimal spatially nonuniform mixture of SAI with CCT might have the potential to better mitigate climate change at both the global and regional scales.

Increases in atmospheric carbon dioxide cause increase in both global temperatures and precipitation. Solar geoengineering has been proposed as a means to counteract this climate change by deliberately deflecting more sunlight from the Earth's climate system. Numerous climate modeling studies have shown that proposed solar geoengineering schemes, such as injection of sulfate aerosols into the stratosphere, can cool climate, but the amount of precipitation change per degree of temperature change is greater than that for CO 2 , meaning that such proposals cannot simultaneously globally restore both average temperatures and average precipitation. It has also been suggested that the Earth could be cooled by thinning cirrus clouds, but the amount of precipitation change per degree of temperature change for this method is less than that for CO 2 . Our climate modeling study shows, for the first time, that a cocktail of these two approaches would decrease precipitation and temperature in the same ratios as they are increased by CO 2 , which would allow simultaneous recovery of preindustrial temperature and precipitation in a high CO 2 world at global scale. We show that although the average temperatures and precipitation can be recovered at global scale, substantial differences between the geoengineered and natural climates persist at regional scale.

1 Introduction Continued emissions of anthropogenic CO 2 from fossil fuel burning and land use increase the burden of atmospheric CO 2 and the amount of global warming [IPCC Climate Change, 2013]. Efforts to mitigate anthropogenic CO 2 emissions are challenging [Clarke et al., 2014], and global warming would persist for millennia even if CO 2 emissions stop [Archer et al., 2009; Frölicher et al., 2014]. Solar geoengineering, also referred to as albedo modification or solar radiation management, has been proposed as a potential means of rapidly preventing some undesirable aspects of anthropogenic climate change [Keith, 2000; Crutzen, 2006; National Research Council, 2015]. The essential idea of solar geoengineering is to deliberately deflect more sunlight away from the Earth to cool the planet. A variety of methods has been proposed under the framework of solar geoengineering, including injection of scattering aerosols into the stratosphere [Budyko, 1977], deployment of an array of mirrors in space [Early, 1989], seeding marine stratocumulus clouds [Latham, 1990], and increasing the albedo of ocean [Seitz, 2011] or land surface [Gaskill, 2004]. In contrast to these solar geoengineering schemes that seek to modify shortwave radiation fluxes, Mitchell and Finnegan [2009] proposed a form of radiation management that seeks to modify longwave radiation. They proposed that one could intentionally reduce the coverage of cirrus clouds and their optical thickness, thus allowing more longwave radiation to escape to space and cool the planet. This method has come to be known as “cirrus cloud thinning” or CCT. Many modeling studies have examined climate effects of solar geoengineering. The consensus is that some aspects of anthropogenic climate change are likely to be mitigated by solar geoengineering, but there are trade‐offs between changes in temperature and hydrology [Bala et al., 2008; National Research Council, 2015]. Climate models participating the Geoengineering Model Intercomparison Project all show a reduction in global mean precipitation when sunshade geoengineering via a globally uniform reduction in insolation is modeled to offset CO 2 ‐induced increase in global mean surface temperature [Kravitz et al., 2013a; Tilmes et al., 2013]. A weakened hydrological cycle is also reported by studies simulating stratospheric aerosol injection [e.g., Niemeier et al., 2013; Kalidindi et al., 2014; Ferraro and Griffiths, 2016] and marine cloud brightening [e.g., Rasch et al., 2009; Bala et al., 2010; Latham et al., 2012; Niemeier et al., 2013]. From an energetic perspective, solar geoengineering reduces the amount of solar radiation reaching the surface, which is primarily balanced by a reduction in latent heat flux to the atmosphere, decreasing global precipitation [Bala et al., 2008; Kravitz et al., 2013b]. An increase in moist static stability in response to solar geoengineering also explains the reduction in global precipitation in a geoengineered world [Kravitz et al., 2013a]. In contrast, modeling studies show that cirrus cloud thinning reduces CO 2 ‐induced warming and at the same time causes an increase in global precipitation [e.g., Crook et al., 2015; Kristjánsson et al., 2015; Jackson et al., 2016]. The intensified hydrological cycle in response to cirrus cloud thinning can be attributed to the increase in net shortwave radiation at surface, which drives more evaporation and thus precipitation [Jackson et al., 2016]. In this study we investigate the potential of cocktail geoengineering, which refers to a mixture of different geoengineering approaches, to simultaneously counteract changes in both temperature and precipitation. We use a climate model to simulate climate effects of cocktail geoengineering that combines stratospheric aerosol increase (SAI) and cirrus cloud thinning (CCT). To our knowledge, this is the first attempt in the peer‐reviewed literature that examines the climate effect of cocktail geoengineering. We design cocktail geoengineering under highly idealized warming scenarios to illustrate the possibility that global mean temperature and precipitation can be restored simultaneously to the baseline state without CO 2 perturbation. We also examine the global and regional climate implications of cocktail geoengineering.

2 Methods 2.1 Model We performed idealized geoengineering simulations using the Community Earth System Model, CESM. In this study, we used the slab ocean model version of CESM 1.2.2 which includes the Community Atmosphere Model version 4 (CAM4) [Neale et al., 2010], the Community Land Model version 4 [Oleson et al., 2010], a slab ocean model, and the Los Alamos Sea Ice Model. CAM4 uses the finite volume dynamic core with a hybrid sigma‐pressure vertical coordinate. The model has a horizontal resolution of 1.9° latitude by 2.5° longitude with 26 vertical levels. The model treats five species of aerosols: sulfate, sea salt, soil dust, black, and organic carbonaceous aerosols. In the model setting, aerosol concentrations are not simulated, and instead, a three‐dimensional climatology background distribution is prescribed for each species of aerosols. The model default background sulfate aerosols are ammonium sulfate (NH 4 ) 2 SO 4 that is assumed to be lognormally distributed with a dry median radius of 0.05 μm and a geometric standard deviation of 2.0. The model default background sulfate aerosol amounts to 0.6 megaton (Mt). CAM4 simulates direct and semidirect effect of aerosols but does not include the indirect effect involving interaction with cloud microphysics. CAM4 has prognostic equations for the liquid and ice‐phase cloud condensate with the process of advection and sedimentation of liquid droplet and ice particles. Ice particle velocity is modeled as a function of temperature‐dependent effective radius. 2.2 Simulations Taylor et al., 2012 2 (abrupt4 × CO 2 ). A set of geoengineering simulations was then performed on top of the abrupt quadrupling of atmospheric CO 2 : SAI simulation in which an additional 44 Mt sulfate aerosol (SAI44) in the form of (NH 4 ) 2 SO 4 is added in the model top stratosphere layer (~3.5 hPa ≈ 38 km) to offset CO 2 ‐induced global mean surface temperature change. We note that we do not inject aerosol precursors, but instead, we directly increase aerosol burdens in the stratosphere, and the added aerosols are not transported around. CCT simulations modeled by uniform increase of cirrus cloud ice particle falling speed by a factor of 2 (2 × CCT), 4 (4 × CCT), and 8 (8 × CCT). Another simulation (maxCCT) was conducted in setting falling speed to be 1020 m s−1 to test the upper bound effect of CCT. Cocktail geoengineering simulations that mix SAI and CCT to offset CO 2 ‐induced global mean or land mean changes in temperature and precipitation simultaneously: SAI29 + 4 × CCT is designed to offset global mean changes in temperature and precipitation simultaneously by combining 29 Mt sulfate aerosol addition with an increase in ice particle falling speed by a factor of 4; SAI38 + 1.5 × CCT is designed to offset land mean changes in temperature and precipitation simultaneously by combining 38 Mt sulfate aerosol addition with an increase in ice particle falling speed by a factor of 1.5. All the above simulations were also performed in the fixed SST (sea surface temperature) mode for 50 years to make an estimate of radiative forcing using “fixed SST” method [Hansen et al., 1997 S4. A set of CESM simulations was performed in slab ocean mode with prescribed ocean heat transport (Q flux). The CESM model in the slab ocean mode takes approximately 30 years to reach a quasi‐equilibrium climate state. Here all simulations were conducted for 100 years and the results averaged over the last 70 years were used for the analysis of equilibrium response. Following the protocols of the Coupled Model Intercomparison Project Phase 5 [.,], a warming simulation was performed with abrupt quadrupling of atmospheric COA set of geoengineering simulations was then performed on top of the abrupt quadrupling of atmospheric COAll the above simulations were also performed in the fixed SST (sea surface temperature) mode for 50 years to make an estimate of radiative forcing using “fixed SST” method [.,]. A detailed description of the simulation setup, as well as the method used to determine the amount of SAI and CCT in cocktail geoengineering, is provided in the supporting information . Table 1 summarizes the simulation setups and model results with additional information given in Tables S1 Table 1. Model Setup and Key Results piControl abrupt4 × CO 2 SAI44 4 × CCT SAI29 + 4 × CCT SAI38 + 1.5 × CCT Atmospheric CO 2 relative to piControl 1 × CO 2 4 × CO 2 4 × CO 2 4 × CO 2 4 × CO 2 4 × CO 2 Stratospheric sulfate aerosols added (Mt) 0 0 44 0 29 38 Cloud ice particle falling speed relative to piControl 1× 1× 1× 4× 4× 1.5× Global mean temperature change (K) 0 6.50 ± 0.02 0.05 ± 0.03 4.77 ± 0.02 0.00 ± 0.03 0.01 ± 0.02 Global mean precipitation change (%) 0 11.87 ± 0.04 −4.64 ± 0.04 11.09 ± 0.05 −0.35 ± 0.06 −3.31 ± 0.03 RMS grid‐scale temperature change over globe (K) 0 7.14 0.68 5.29 0.62 0.58 RMS grid‐scale precipitation change over globe (mm day −1) 0 0.81 0.39 0.66 0.41 0.36

3 Results Figure 1 shows changes in global mean and land mean temperature versus that of precipitation. In our simulations, a quadrupling of atmospheric CO 2 increases global mean surface temperature by 6.50 ± 0.02 K (uncertainty is represented by one standard error). A spatially uniform increase in stratospheric sulfate aerosols causes an uneven distribution of radiative forcing: relative to piControl, a uniform increase of 44 Mt stratospheric sulfate aerosol on top of 4 × CO 2 causes a large negative forcing at low latitudes and positive forcing at high latitudes (Figure S1). By design, a uniform increase of 44 Mt sulfate aerosol in the upper stratosphere (SAI44), by reflecting solar radiation back to space (Figures S2 and S3), offsets CO 2 ‐induced temperature change with a slight residual global mean warming of 0.05 ± 0.03 K above the preindustrial climate. Meanwhile, SAI44 produces a decrease in global mean precipitation of 4.6 ± 0.04% relative to preindustrial state (Figure 1) with the decrease more pronounced over the oceans than over land (Figures S4 and S5). Figure 1 Open in figure viewer PowerPoint piControl) of (a) global mean and (b) land mean temperature versus precipitation for simulations of CO 2 warming (abrupt4 × CO 2 ), stratospheric aerosol increase that offset CO 2 ‐induced global mean warming (SAI44), cirrus cloud thinning (2 × CCT, 4 × CCT, and 8 × CCT), and mixed stratospheric aerosol increase and cirrus cloud thinning (SAI29 + 4 × CCT offsets CO 2 ‐induced changes in both global mean temperature and precipitation simultaneously; SAI38 + 1.5 × CCT offsets CO 2 ‐induced changes in both land mean temperature and precipitation simultaneously). All results are annual mean values averaged over simulation years of 31–100. Experiment details and numerical values with uncertainties are provided in Table abrupt4 × CO 2 and SAI44 (green lines), abrupt4 × CO 2 and 4 × CCT (brown lines), abrupt4 × CO 2 and SAI29 + 4 × CCT (blue lines), and abrupt4 × CO 2 and SAI38 + 1.5 × CCT (red lines) are plotted with the numbers representing the slope (hydrological sensitivity) of the line. Here hydrological sensitivity (HS) is defined to be the ratio of global (land) mean precipitation change to global (land) surface temperature change. The values of HS with uncertainties for different simulations are provided in Table abrupt4 × CO 2 . Thus, a combination of both schemes, such as SAI29 + 4 × CCT and SAI38 + 1.5 × CCT, has a HS close to that of abrupt4 × CO 2 (Table 2 ‐induced changes in global (land) mean temperature and precipitation simultaneously. Model‐simulated changes (relative to) of (a) global mean and (b) land mean temperature versus precipitation for simulations of COwarming (), stratospheric aerosol increase that offset CO‐induced global mean warming (), cirrus cloud thinning (, and), and mixed stratospheric aerosol increase and cirrus cloud thinning (offsets CO‐induced changes in both global mean temperature and precipitation simultaneously;+ 1.5 × CCT offsets CO‐induced changes in both land mean temperature and precipitation simultaneously). All results are annual mean values averaged over simulation years of 31–100. Experiment details and numerical values with uncertainties are provided in Table 1 . Lines crossing the points ofand(green lines),and(brown lines),and(blue lines), andand+ 1.5 × CCT (red lines) are plotted with the numbers representing the slope (hydrological sensitivity) of the line. Here hydrological sensitivity (HS) is defined to be the ratio of global (land) mean precipitation change to global (land) surface temperature change. The values of HS with uncertainties for different simulations are provided in Table S5 . It can be seen that stratospheric aerosol geoengineering has a larger HS, and cirrus cloud geoengineering has a smaller HS, than that of. Thus, a combination of both schemes, such asand+ 1.5 × CCT, has a HS close to that of(Table S5 ) and is able to offset CO‐induced changes in global (land) mean temperature and precipitation simultaneously. CCT geoengineering can only partly offset warming of abrupt4 × CO 2 (Figure 1), which is inferred from the estimated radiative forcing of 4 × CCT (Figure S1) that only offsets a fraction of CO 2 ‐induced positive forcing. In our simulations, artificially increasing falling speed of cirrus cloud ice particles reduces the amount of cloud cover, particularly the high‐level cloud (Figures S6 and S7), allowing more outgoing longwave radiation (OLR) to escape to the space (Figures S8 and S9). On the other hand, the reduced cloud cover increases solar forcing at TOA (Figures S8 and S9), partly compensating the cooling effect from increased OLR. With increasing ice particle falling speed, the amount of cirrus cloud cover decreases and further increase in falling speed would have a smaller effect on cirrus cloud cover. Therefore, the relative decrease in high‐level cloud cover and radiative fluxes becomes smaller as the ice particle falling speed increases further (compare the changes in high cloudiness and radiative fluxes between 2 × CCT, 4 × CCT, 8 × CCT, and maxCCT in Figures S6, S7, and S9). As a result, the relationship between CCT‐induced cooling and the amount of change in ice particle falling speed is highly nonlinear (Figures S10 and S11): relative to abrupt4 × CO 2 , a global cooling of 0.95 ± 0.03, 1.73 ± 0.03, and 2.10 ± 0.04 K is achieved by a 2×, 4×, and 8× increase in ice particle falling speed. Relative to the preindustrial state, all CCT simulations show an increase in global temperature and precipitation (Figure S11). We estimate the apparent hydrological sensitivity [Fläschner et al., 2016], defined as the percentage change in global mean precipitation per unit of global mean temperature change, for CO 2 , SAI, and CCT forcing (Figure 1 and Table S5). For abrupt4 × CO 2 , the hydrological sensitivity is 1.83 ± 0.01% K−1 (calculated relative to piControl), whereas it is 2.56 ± 0.01% K−1 for SAI44, and 0.57 ± 0.04, 0.45 ± 0.04, and 0.30 ± 0.03% K−1 for 2 × CCT, 4 × CCT, and 8 × CCT, respectively (calculated relative to abrupt4 × CO 2 ). The fact that SAI has a larger hydrological sensitivity, and CCT has a lower sensitivity than do changes in atmospheric CO 2 , opens up the possibility that SAI and CCT geoengineering could be combined together to simultaneously offset changes in both precipitation and temperature caused by increase in atmospheric CO 2 . We designed cocktail geoengineering to stabilize temperature and precipitation simultaneously (refer to supporting information for details). In our simulations, an additional mass of 29 Mt sulfate aerosols in the upper stratosphere combined with a fourfold increase in ice particle falling speed (SAI29 + 4 × CCT) offset CO 2 ‐induced changes in global temperature and precipitation simultaneously (Figure 1). As expected, the apparent hydrological sensitivity for SAI29 + 4 × CCT is about the same as that of abrupt4 × CO 2 (Table S5). We also designed cocktail geoengineering in which an additional mass of 38 Mt SO 4 combined with 1.5 × increase in ice particle falling speed (SAI38 + 1.5 × CCT) offset land mean temperature and precipitation change simultaneously (Figure 1). Following the framework of Bala et al. [2008] and Kravitz et al. [2013b], we analyze the reasons for different precipitation responses to SAI, CCT, and mixed SAI and CCT geoengineering from the perspective of energy constraints. Global mean precipitation is tightly related to the amount of latent flux to the atmosphere. At equilibrium state with a zero net surface energy balance, the sum of global and annual mean latent and sensible heat fluxes is equal to the net radiative flux at the surface. In the simulation of SAI44, since temperature change is small, the change in net longwave radiation at the surface is small (Figure 2b). Meanwhile, increased burden of stratospheric sulfate aerosols reduces the amount of solar radiation reaching the surface (Figure 2a), which is largely compensated by the reduction in latent heat flux to the atmosphere (Figure 2c). In the simulation of 4 × CCT, a thinner cirrus cloud allows more solar radiation to reach the surface (Figure 2a). Meanwhile, a warmer atmosphere (relative to piControl) increases net downward longwave radiative flux at the surface (Figure 2b). Increased downward solar and longwave radiation at surfaces in 4 × CCT is largely compensated by an increase in latent heat flux to the atmosphere (Figure 2c). In the cocktail geoengineering simulation that stabilizes global mean temperature and precipitation change simultaneously (SAI29 + 4 × CCT), CCT‐induced increase in solar radiation dominates SAI‐induced decrease in solar radiation, causing a small increase in net downward solar radiation at surface (Figure 2a). Meanwhile, the change in net longwave flux at surface is rather small (Figure 2b) because of small temperature change. As shown in Figures 2c and 2d, in the simulation of SAI29 + 4 × CCT, the small increase in downward solar radiation at surface is largely balanced by a small increase in sensible heat flux to the atmosphere with an even smaller change in latent heat flux. The tiny change in latent heat flux explains the near‐zero global precipitation change in the cocktail geoengineering case of SAI29 + 4 × CCT. Figure 2 Open in figure viewer PowerPoint piControl) of global mean energy fluxes at surface. All fluxes are defined to be positive down. All results are annual mean values averaged over simulation years of 31–100. Simulation cases are the same as described in Figure Model‐simulated equilibrium change (relative to) of global mean energy fluxes at surface. All fluxes are defined to be positive down. All results are annual mean values averaged over simulation years of 31–100. Simulation cases are the same as described in Figure 1 When zonal mean climate change is considered, relative to piControl, stratospheric aerosol increase (SAI44) produces an overcooling in the tropical regions and residual warming at high latitude with a substantial decrease in precipitation over the tropical ocean (Figures 3 and S12). Compared to SAI44, the mixture of SAI and CCT that offsets both global mean temperature and precipitation (SAI29 + 4 × CCT) has a small effect on the large‐scale pattern of temperature response (Figure 3). Relative to SAI44, cocktail geoengineering produces an overall increase of precipitation, especially in the tropics (Figures 3 and S12). Corresponding spatial distribution of temperature and precipitation change is shown in Figure S13. Figure 3 Open in figure viewer PowerPoint piControl. (a, c) Zonal mean over globe (land + ocean). (b, d) Zonal mean over land. Equal area is used in the abscissa. All results are annual mean values averaged over simulation years 31–100. Shading represents ±1 standard deviation calculated from years 31–100 results of piControl. Simulation cases are the same as described in Figure Model‐simulated zonal mean changes in temperature and precipitation relative to(a, c) Zonal mean over globe (land + ocean). (b, d) Zonal mean over land. Equal area is used in the abscissa. All results are annual mean values averaged over simulation years 31–100. Shading represents ±1 standard deviation calculated from years 31–100 results of. Simulation cases are the same as described in Figure 1 . Corresponding zonal mean plots over oceans are provided in Figure S12 In our simulations the climate effect of SAI and CCT forcing is roughly additive. Relative to abrupt4 × CO 2 , simulated climate response to the combined SAI and CCT forcing is similar to the linear combination of climate response to SAI and CCT forcing (Figure S14). For example, out of the 6.50 ± 0.02 K warming induced by abrupt4 × CO 2 , SAI29, and 4 × CCT offset 4.31 ± 0.02 and 1.72 ± 0.02 K, respectively, with the remaining 0.47 ± 0.03 K associated with a nonlinear feedback between SAI29 and 4 × CCT (Table S6). The approximate linearity of climate response to SAI and CCT geoengineering also holds at the zonal mean level (Figure S15). Both SAI and CCT modify radiative fluxes and thermal structure of the atmosphere, which in turn affect the climate impact of SAI and CCT geoengineering. Therefore, some nonlinear interactions between SAI and CCT geoengineering are expected. Kravitz et al., 2013a abrupt4 × CO 2 . RMS is defined as follows: (1) V exp is a climate variable in a model grid cell in abrupt4 × CO 2 or one of the geoengineering simulations, and V ctr is corresponding variable in piControl. dA is the area of a grid cell. Here we calculate NRMS of temperature and precipitation for all global cells or land‐only cells. As shown in Figures abrupt4 × CO 2 , indicating that geoengineering reduces the overall departure of climate change from preindustrial state. All geoengineering simulations are much more effective in restoring temperature than precipitation. Relative to the simulation with only sulfate aerosol (SAI44), it does not appear that cocktail geoengineering of SAI29 + 4 × CCT or SAI38 + 1.5 × CCT is more effective in restoring global climate as a whole. These results indicate that restoring global mean or land mean temperature and precipitation simultaneously does not necessarily produce an overall climate state that is closer to the preindustrial state. Pattern differences in different simulations compared with piControl are summarized in Taylor diagrams (Figures abrupt4 × CO 2 , the spatial pattern of geoengineered climate is closer to the preindustrial state. However, the overall pattern difference between SAI and mixed SAI and CCT geoengineering simulations is small. The overall effectiveness of geoengineering in restoring global climate to the baseline state can be assessed using the normalized root‐mean‐square difference (NRMS) [.,]. That is, area‐weighted RMS difference of a geoengineering simulation divided by the RMS difference of. RMS is defined as follows:whereis a climate variable in a model grid cell inor one of the geoengineering simulations, andis corresponding variable in. dis the area of a grid cell. Here we calculate NRMS of temperature and precipitation for all global cells or land‐only cells. As shown in Figures 4 a and 4 b, RMS of temperature and precipitation for all geoengineering simulations are less than that of, indicating that geoengineering reduces the overall departure of climate change from preindustrial state. All geoengineering simulations are much more effective in restoring temperature than precipitation. Relative to the simulation with only sulfate aerosol (), it does not appear that cocktail geoengineering ofor+ 1.5 × CCT is more effective in restoring global climate as a whole. These results indicate that restoring global mean or land mean temperature and precipitation simultaneously does not necessarily produce an overall climate state that is closer to the preindustrial state. Pattern differences in different simulations compared withare summarized in Taylor diagrams (Figures 4 c and 4 d). Overall, compared to climate of, the spatial pattern of geoengineered climate is closer to the preindustrial state. However, the overall pattern difference between SAI and mixed SAI and CCT geoengineering simulations is small. Figure 4 Open in figure viewer PowerPoint piControl, and NRMS is the RMS normalized by the RMS of abrupt4 × CO 2 . Therefore, NRMS for abrupt4 × CO 2 is one. Taylor diagrams compare spatial pattern of grid‐scale temperature and precipitation over (c) globe and (d) land with that of piControl, which is denoted by the cross symbol at (1,1). The radial distance from the origin denotes standard deviation; correlation coefficient is given by the azimuth; and the centered RMS difference is proportional to the distance to the point of piControl. Standard deviation and RMS are normalized by the standard deviation of piControl. All statistics are calculated using annual mean values averaged over simulation years 31–100. Note that for the Taylor diagram only the lower portion is shown here because all correlation coefficients are greater than 0.8. Corresponding full scale Taylor diagrams are shown in Figure Normalized root‐mean‐square differences (NRMS) of grid‐scale surface temperature versus that of grid‐scale precipitation calculated over (a) globe and (b) land. RMS is calculated relative to, and NRMS is the RMS normalized by the RMS of. Therefore, NRMS foris one. Taylor diagrams compare spatial pattern of grid‐scale temperature and precipitation over (c) globe and (d) land with that of, which is denoted by the cross symbol at (1,1). The radial distance from the origin denotes standard deviation; correlation coefficient is given by the azimuth; and the centered RMS difference is proportional to the distance to the point of. Standard deviation and RMS are normalized by the standard deviation of. All statistics are calculated using annual mean values averaged over simulation years 31–100. Note that for the Taylor diagram only the lower portion is shown here because all correlation coefficients are greater than 0.8. Corresponding full scale Taylor diagrams are shown in Figure S16

4 Discussion and Conclusions In this study, we focus on modeled climate response and have not considered the engineering aspects of any geoengineering schemes, which are beyond the scope of this study. Unexpected environmental risks could also arise from SAI and CCT geoengineering, and the mixture of the two. A thorough investigation of these potential side effects is also beyond our scope. The stabilization of both temperature and precipitation through cocktail geoengineering relies on the assumption that SAI and CCT geoengineering work as expected. Numerous modeling studies have demonstrated the effect of stratospheric aerosol geoengineering on temperature and hydrological cycle [National Research Council, 2015, and references therein], which is further illustrated by the imperfect analog of volcanic eruption [Rasch et al., 2008]. Our simulated climate response to CCT geoengineering is in broad agreement with previous studies in that CCT reduces CO 2 ‐induced warming without substantially weakening the global hydrological cycle [Muri et al., 2014; Kristjánsson et al., 2015; Crook et al., 2015; Jackson et al., 2016]. However, only a few studies have evaluated the climate effect of CCT, which merits further modeling studies preferably with improved representation of cloud microphysics [Bardeen et al., 2013]. The degree to which manipulation of cirrus clouds would be able to achieve a substantial negative radiative forcing is uncertain, and estimates of this efficacy depend on details of the model formulation [Gasparini and Lohmann, 2016]. Also, cautions should be taken in interpreting results from CCT simulations via increasing the falling speed of ice particles. It is found that to increase the falling speed of cirrus ice crystals might not be a good proxy for cirrus cloud seeding since an increase in ice particle falling speed cannot explicitly represent the competition between homogeneous and heterogeneous ice particle nucleation [Gasparini et al., 2017]. Furthermore, in our simulations additional sulfate aerosols are prescribed in the upper stratosphere. If aerosol precursors such as SO 2 is injected into the stratosphere, transport of formed stratospheric aerosol into upper troposphere could affect optical properties of cirrus cloud via cloud‐aerosol interaction and changes in environmental temperature [Kuebbeler et al., 2012; Cirisan et al., 2013]. This effect of stratospheric aerosol injection on cirrus cloud is not considered here. Nevertheless, the basic points we make here about relations between temperature and precipitation changes in the cocktail geoengineering scenarios are likely to be robust, but the maximum feasible scale of application of a cocktail geoengineering approach might be substantially less than what is presented in our idealized simulations. Here we used a highly idealized warming scenario of 4 × CO 2 to illustrate the possibility of adopting mixed SAI and CCT geoengineering to stabilize global mean temperature and precipitation simultaneously. To stabilize global mean temperature and precipitation under a different background CO 2 scenario would require different combinations of SAI and CCT geoengineering. Thus, with the goal of stabilizing global temperature and precipitation simultaneously, cocktail geoengineering might result in different regional climate change under different atmospheric CO 2 background states. It might be also possible to design cocktail geoengineering schemes to offset CO 2 ‐induced changes in other pairs of climate fields simultaneously, such as land mean temperature and runoff. Here we tested geoengineering scenarios with spatially uniform addition of stratospheric aerosols and scaling of cirrus cloud ice particle falling speed, both of which are highly idealized. An uneven distribution of stratospheric aerosol loading has the potential to better stabilize some aspects of CO 2 ‐induced climate change [e.g., Ban‐Weiss and Calderia, 2010; MacMartin et al., 2012]. Therefore, by optimizing distribution of SAI and CCT geoengineering, we might be able to design cocktail geoengineering schemes that better meet our climate mitigation target, such as changes in temperature, precipitation, water availability, and crop yields. The mixture of other types of geoengineering approaches, such as marine cloud brightening and CCT, might also be a choice for cocktail geoengineering strategy that merits further studies.

Acknowledgments Long Cao and Lei Duan are supported by National Key Basic Research Program of China (2015CB953601) and National Natural Science Foundation of China (41422503). All data and CESM results used in the paper are archived at supercomputer center at Zhejiang University, which can be obtained by contacting longcao@zju.edu.cn.

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