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Keywords:

  • aerosol direct effect;
  • aerosol indirect effect;
  • cloud modification;
  • geoengineering;
  • sea spray

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[1] Climate-aerosol model ECHAM5.5-HAM2 was used to investigate how geoengineering with artificial sea salt emissions would affect marine clouds and the Earth's radiative balance. Prognostic cloud droplet number concentration and interaction of aerosol particles with clouds and radiation were calculated explicitly, thus making this the first time that aerosol direct effects of sea spray geoengineering are considered. When a wind speed dependent baseline geoengineering flux was applied over all oceans (total annual emissions 443.9 Tg), we predicted a radiative flux perturbation (RFP) of −5.1 W m−2, which is enough to counteract warming from doubled CO2 concentration. When the baseline flux was limited to three persistent stratocumulus regions (3.3% of Earth's surface, total annual emissions 20.6 Tg), the RFP was −0.8 Wm−2 resulting mainly from a 74–80% increase in cloud droplet number concentration and a 2.5–4.4 percentage point increase in cloud cover. Multiplying the baseline mass flux by 5 or reducing the injected particle size from 250 to 100 nm had comparable effects on the geoengineering efficiency with RFPs −2.2 and −2.1 Wm−2, respectively. Within regions characterized with persistent stratocumulus decks, practically all of the radiative effect originated from aerosol indirect effects. However, when all oceanic regions were seeded, the direct effect with the baseline flux was globally about 29% of the total radiative effect. Together with previous studies, our results indicate that there are still large uncertainties associated with the sea spray geoengineering efficiency due to variations in e.g., background aerosol concentration, updraft velocity, cloud altitude and onset of precipitation.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[2] The fear of abrupt changes in the climate system as the anthropogenic greenhouse gases keep accumulating in the atmosphere has in recent years motivated several proposals to control climate change by deliberately manipulating the Earth's albedo [Lenton and Vaughan, 2009]. These proposed methods are commonly referred to as geoengineering. One of the much-discussed methods is to use artificial sea spray emissions from wind powered vessels in order to increase the concentration of submicron sea salt particles in the marine boundary layer [Latham, 1990]. It has been hypothesized that these artificially emitted aerosol particles could act as cloud condensation nuclei (CCN) and thus increase the cloud droplet number concentration (CDNC) in marine stratocumulus clouds. This in turn would lead, at least in theory, to a higher cloud albedo [Twomey, 1974] and thus planetary cooling.

[3] Previous climate model studies investigating the climatic effects of the proposed cloud whitening have concluded that the technique could counteract either all or at least a large fraction of the radiative forcing from a doubled CO2 concentration (+3.7 Wm−2 [Forster et al., 2007]). Latham et al. [2008] calculated the global mean forcing to be −8 Wm−2 when a CDNC of 375 cm−3 in all marine clouds below about 3 km was assumed. Jones et al. [2009] assumed sea spray geoengineering over only 3.3% of the Earth's surface and found that global warming could be postponed by 25 years. On the other hand, their study warned of potentially detrimental side effects, such as a sharp decrease of precipitation over the South American continent and especially in the Amazon region. While Rasch et al. [2009]did not predict as abrupt a precipitation trend in this region, they concluded that it is not possible to bring the surface temperature, precipitation and sea ice extent simultaneously back to their pre-industrial state using sea spray geoengineering, although the method does have potential to counteract global warming.

[4] One shortcoming of the previously described studies is that they all assumed a fixed value of 375 or 1000 cm−3 for CDNC in the geoengineered regions without explicit considerations of the emissions, microphysics and dry deposition of artificially produced sea salt particles. Including these effects in a chemical transport model (CTM) study, Korhonen et al. [2010] found that achieving a previously assumed uniform distribution of high CDNC would be extremely difficult due to wind speed dependence of the emission flux, aerosol removal processes and interactions between injected sea salt particles with the background aerosol. A drawback in this study was, however, that as it used a CTM and thus did not describe the feedback from injected aerosol particles to cloud properties, it was unable to quantify the resulting radiative forcing or any feedbacks from clouds to aerosols.

[5] The aerosol-cloud interactions were studied in detail byWang et al. [2011], who used a cloud-system-resolving model to investigate the efficacy of cloud seeding in various meteorological conditions. Their results indicate that sea spray geoengineering can be efficient in weakly precipitating conditions, in which particle injections can suppress rain formation, as well as in CCN-limited conditions. On the other hand, the albedo enhancement is likely to be inefficient in strongly precipitating, polluted or water-vapor-limited conditions. These cloud-resolving simulations further support the conclusion ofKorhonen et al. [2010] that obtaining homogeneous CDNC fields over large areas is very unlikely.

[6] Despite the variety of tools used to investigate the artificial cloud whitening so far, none of the previous studies has quantified the aerosol direct effect of sea spray geoengineering, i.e., the effect on the global radiation balance through scattering and absorbing of solar and terrestrial radiation. The optimal sea spray dry diameter for cloud seeding has been estimated to lie in the range 200 nm to 1 μm [Latham et al., 2008]. Particles of this size also efficiently scatter solar radiation, and the injected sea salt particles transported outside heavily clouded regions are thereby expected to affect the planetary albedo directly. Bower et al. [2006] used a cloud parcel model to test how the injected particle size affects cloud droplet activation, but the previous global model studies have not investigated the optimal seeding strategy in terms of particle size. On the one hand, the cloud albedo effect per unit mass emissions can be expected to be the larger the smaller the emitted particles are because of increasing number emissions with decreasing size (as long as they are still large enough to activate as cloud droplets). On the other hand, particles in the size range of 100 nm or smaller contribute little to the aerosol direct forcing.

[7] In this study, we use the aerosol-climate model ECHAM5.5-HAM2 to investigate the two previously neglected aspects of sea spray geoengineering mentioned above. We will (1) assess the relative importance of aerosol direct and indirect effects in different oceanic regions, and (2) study how the magnitude of the injection and the injected particle size affect the clouds and Earth's energy balance. Since running an explicit size-resolved aerosol description inside a global climate model is computationally very expensive, we have to use climatological values for sea surface temperature and sea ice extent and will thereby not predict climatic effects such as precipitation or surface temperature changes. Furthermore, this study does not address any of the many other environmental, ethical and political problems related to geoengineering [e.g.,Robock, 2008].

2. Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

2.1. Aerosol-Climate Model ECHAM5.5-HAM2

[8] We used the aerosol-climate model ECHAM5.5-HAM2 [Stier et al., 2005; K. Zhang et al., 2011, The global aerosol-climate model ECHAM5-HAM, version 2 (ECHAM5-HAM2): Model description and evaluation, manuscript in preparation, 2012] in our simulations. The aerosol module HAM2 includes an explicit modal aerosol scheme M7 [Vignati et al., 2004] to calculate aerosol processes of hydration, nucleation, condensation and coagulation. It has seven lognormal modes which describe both externally and internally mixed aerosol populations. The aerosol species considered are sulfate, sea salt, black carbon, organic carbon and mineral dust. In this study, the aerosol emissions from anthropogenic sources and biomass burning were taken from the AEROCOM database for year 2000 [Dentener et al., 2006]. For natural sea spray emissions in the accumulation and coarse mode range, we used a parameterization combining the wind speed-dependent source functions byMonahan et al. [1986] and by Smith and Harrison [1998] [Schulz et al., 2004]. Dust emissions were also calculated online using Tegen et al.'s [2002] scheme. Cloud droplet activation of the aerosol population was calculated using a physically based parameterization by Abdul-Razzak and Ghan [2000] and cloud microphysics as described by Lohmann and Hoose [2009]. Updraft velocity for stratiform clouds was parameterized as the sum of the grid mean vertical velocity and a turbulent part, which was expressed in terms of prognostic turbulent kinetic energy [Lohmann and Hoose, 2009]. Cloud cover was diagnosed as a function of relative humidity by using an empirical parameterization by Sundqvist et al. [1989]. With this model setup, it is for the first time possible to evaluate the radiative forcing of the sea spray geoengineering method starting from a defined source function for artificially produced sea spray emissions and calculating explicitly both the microphysical processes of injected particles and their effects on clouds and atmospheric dynamics.

[9] Simulations presented in section 2.3had a one-year spin-up period, after which the model was run for 10 years for each simulation. The model horizontal resolution was T63 which corresponds approximately to a 1.9° × 1.9° grid. The atmosphere extended to a pressure level of 10 hPa and was divided into 31 vertical levels.

2.2. Source Function for Artificial Sea Salt Emissions

[10] It is still uncertain how sufficiently high fluxes of artificial sea spray particles for substantial climate cooling could be achieved in practice. Thus far the only concrete proposal of potential injection vessel design has been put forward by Salter et al. [2008]who suggested a fleet of 1500 unmanned and wind-powered ships operating on Flettner rotors. Therefore, we implemented in the geoengineering simulations an additional source function of sea salt which has the same wind speed dependence as has been suggested by S. Salter (personal communication, 2008) and also used byKorhonen et al. [2010]. We defined our baseline mass flux as:

  • display math

where uis the 10-m wind speed. In most of our simulations, we used a dry geometric mean diameter of 250 nm (dry particle mean mass 4.7 × 10−17 kg) which resulted in number flux:

  • display math

Note that the simulated mass and number fluxes are 134% and 10% higher, respectively, than the corresponding fluxes in the Korhonen et al. [2010] study. The larger difference in mass flux is due to the fact that Korhonen et al. [2010] used a sectional aerosol model and emitted the particles at a monodisperse size (dry diameter 260 nm), whereas HAM2 is a modal model in which the particles have to be emitted into a lognormal mode with a prescribed standard deviation (1.59 for the soluble accumulation mode into which the additional sea spray particles were injected).

[11] In addition to the simulations with a dry geometric mean diameter of 250 nm, we conducted a set of sensitivity simulations in which the dry geometric mean diameter of the artificial sea spray emissions was set to either 100 nm or 500 nm while the total mass flux was still given by the baseline flux in equation (1). This approach was chosen to investigate the effect of the size of the injected particles on the efficiency of geoengineering since, on the one hand, larger particles activate to cloud droplets at a lower supersaturation than smaller ones but, on the other hand, higher number flux with smaller particles (when the mass flux is kept constant) can lead to a higher CDNC, provided that the injected particles are large enough to activate. Furthermore, the direct aerosol effect is strongly dependent on the particle size (according to Mie theory, mass scattering efficiency for ambient sea salt particle peaks at diameter of about 690 nm, which at relative humidity of 80% corresponds to dry diameter of about 380 nm). A constant total mass flux was chosen based on the assumption that the primary technical limitation of the sea spray vessels concerns the rate at which seawater can be sprayed into the atmosphere (e.g., Salter et al. [2008] estimate their vessel design could reach a spray rate of 30 kg s−1) while the size of the emitted particles can be relatively freely controlled. Recent research has shown that it is possible to produce crystals down to 75–85 nm size by spraying seawater (J. Latham et al., Marine cloud brightening, submitted to Philosophical Transactions of the Royal Society, 2010).

2.3. Model Experiments

[12] The nine simulations performed in this study are listed in Table 1. The control simulation (CTRL) was run with the standard ECHAM5.5-HAM2 aerosol emissions described insection 2.1. In simulation ALL, we added artificial sea salt emissions according to equation (1)to all ice free parts of the oceans. By calculating the difference in the total-sky radiative fluxes between the ALL and CTRL simulations, we were able to determine the optimal areas in which geoengineering would be likely to give the strongest cooling effect (seesection 2.5for details). These optimized areas are used in all the other geoengineering simulations, apart from the sensitivity simulation UF-SS ALL.

Table 1. List of Simulations in This Studya
Simulation NameAdded Sea Salt FluxesEmission Area for GeoengineeringMean Dry Diam. (nm)
  • a

    “Added Sea Salt Fluxes” describes the sea salt fluxes that are added to model default emissions. For all geoengineering simulations the artificial sea salt flux was given as a multiple of the baseline mass flux (equation (1)). Artificial sea salt emissions were set on either over all ocean areas or only over the three optimized areas (“Emission Area for Geoengineering”). For each simulation the number distribution of sea salt particles from artificial emissions had a fixed standard deviation of 1.59 and a mean dry diameter (“Mean Dry Diam.”). The sensitivity of our results to the lack of ultrafine sea spray emissions in ECHAM5.5-HAM2 model was tested with two sensitivity simulations (UF-SS CTRL and UF-SS ALL).

CTRL---
ALL1 × baseline fluxAll oceans250
GEO1 × baseline fluxOptimized250
3 × GEO3 × baseline fluxOptimized250
5 × GEO5 × baseline fluxOptimized250
SMALL GEO1 × baseline fluxOptimized100
LARGE GEO1 × baseline fluxOptimized500
UF-SS CTRLnatural ultrafine--
UF-SS ALL1 × baseline flux + natural ultrafineAll oceans250

[13] In order to study the effect of emission flux strength on the efficiency of the sea spray method, we performed three geoengineering simulations with different artificial sea spray emissions in the optimized regions: the baseline mass flux given by equation (1) (simulation GEO), and threefold and fivefold baseline mass and number fluxes (3 × GEO and 5 × GEO, respectively). In order to investigate the effect of the injection size of the particles under a constant mass flux assumption, we performed two simulations with injected sea salt particles having a geometric mean dry diameter of 100 and 500 nm (simulations SMALL GEO and LARGE GEO, respectively). In both of these simulations, the source function for injected sea salt mass, as well as the geoengineered regions, were the same as in the simulation GEO. With emitted particle geometric mean dry diameter of 100 nm and 500 nm, the number fluxes from geoengineering were 1560% and 12.5%, respectively, of the baseline number flux given by equation (2).

[14] One of the limitations of the aerosol model HAM2 is that it includes natural sea salt emissions only in the accumulation and coarse modes (particle dry geometric mean diameter > 100 nm). According to model simulations by Pierce and Adams [2006], ultrafine sea spray particles have a significant effect on cloud condensation nuclei number concentration especially in clean oceanic regions and could thus affect the efficiency of sea spray geoengineering. In order to estimate the sensitivity of our results to the lack of ultrafine sea spray emissions, we made two sensitivity simulations in which we crudely estimated the natural ultrafine sea salt emissions. We added these emissions to the model following the flux parameterization of Mårtensson et al. [2003] in this size range: We first integrated the number and mass flux of the parameterization in the size range of the Aitken mode in the model (10 nm < Dp < 100 nm) to get the total fluxes in the ultrafine size range. From the total number and mass flux we then calculated the geometric mean diameter of the emissions to this mode to be 45 nm. The standard deviation of the emitted ultrafine particles was set to 1.59 which is a fixed value in the modal representation of the Aitken mode in HAM2. Natural sea spray emissions in larger sizes were not changed.

[15] Since the standard structure of the HAM2 aerosol model does not allow for sea spray in the Aitken mode, we added the ultrafine sea spray to the model as sulfate. While both sulfate and sea spray are highly water-soluble species, the critical supersaturation for a certain-sized sea spray particle is somewhat lower than for a sulfate particle of the same size. Therefore, this scheme is not meant as an evaluation of the exact effects of the ultrafine sea salt on clouds or climate, but to merely provide a sensitivity test for our geoengineering simulations. Out of the two sensitivity simulations, the simulation without geoengineering emissions was named UF-SS CTRL and the corresponding geoengineering simulation UF-SS ALL. This geoengineering simulation had artificial sea salt emissions covering all oceans.

2.4. Calculating Radiative Effects

[16] In our simulations, artificial sea salt emissions affect the Earth's radiation balance both directly by scattering solar radiation and indirectly by changing cloud properties and atmospheric dynamics. Standard radiative forcing definition by IPCC [Forster et al., 2007] cannot be used to include all these effects since it assumes the tropospheric state to be unaffected by the perturbation. Instead, we use radiative flux perturbation (RFP) [Haywood et al., 2009] to evaluate the total radiative effect of the artificial sea spray emissions. It allows the inclusion of fast feedbacks of the climate system (e.g., change in precipitation patterns) and is thereby suitable for evaluating the radiative effects of aerosol-cloud interactions [Lohmann et al., 2010]. RFP is calculated as the difference in total net radiation (short- and long-wave) at the top of the atmosphere (TOA) between a geoengineering simulation and the control simulation.

[17] It is not possible to fully separate the direct and indirect contributions to RFP because of the nonlinearities in the climate system. However, an estimate for the relative strength of direct and indirect effects can be obtained. We calculated the total-sky aerosol direct forcing (ADF) in all the simulations by calling the online radiation routine twice: with and without the atmospheric aerosols (Figure 1). The difference in net total radiation (short- and long-wave) between the calls is defined as ADF. We used thedifference in ADF (ΔADF) between a geoengineering and the control simulation as a measure of aerosol direct effects. Note that ΔADF is not radiative forcing as defined by IPCC [Forster et al., 2007], because the evolution of the atmospheric dynamics is not the same in different simulations. The estimate for the magnitude of all indirect effects was based on the assumption that the RFP is the sum of the direct (ΔADF) and indirect aerosol effects. Consequently, the indirect effect was calculated by subtracting ΔADF from RFP.

image

Figure 1. Calculation of radiative forcing of aerosols for a single simulation. Aerosol direct forcing (of all atmospheric aerosol particles) is calculated as the difference between net radiation in (right) aerosol free atmosphere and (left) total-sky net radiation. Difference in ADF between a geoengineering simulation and the control simulation is used as a measure of aerosol direct effects.

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[18] Note that the choice to use total-sky fluxes instead of clear-sky fluxes when calculating ADF may lead to positive ΔADF even when there is a large increase in aerosol optical depth. This can happen if the cloud cover is much larger in a geoengineering simulation than in the control simulation as the albedo enhancement due to aerosols is lower in a heavily clouded case. However, in general ΔADF calculated from total-sky fluxes gives a closer estimate of the real cooling effect than calculations using clear-sky fluxes.

2.5. Optimization of Emission Regions

[19] Previous climate model studies of sea spray geoengineering have found significant spatial variation in the resulting shortwave cloud forcing (ΔSWCF) [Latham et al., 2008; Rasch et al., 2009]. Because of the different spatial responses, the most cost-effective strategy to obtain climate cooling with this method is likely to be to modify clouds in carefully selected regions. In a previous study,Rasch et al. [2009] created a monthly varying mask for seeded regions based on the amplitude of ΔSWCF. On the other hand, Jones et al. [2009]based their selection on cloud cover and chose regions with persistent stratocumulus sheets. In general, the previous studies have highlighted the persistent stratocumulus regions off the west coasts of North America, South America and South-West Africa as the most susceptible ones to cloud whitening.

[20] In this study, the optimal regions for sea spray injections were selected based on the following procedure: We constructed an optimization algorithm to select continuous regions from the simulation ALL so that the total-sky radiative flux perturbation (RFP) inside the selected regions was maximized (i.e., the greatest cooling effect was achieved). We initialized the algorithm from the grid cell with the lowest RFP in three regions (North Pacific, South Pacific and South Atlantic) that showed overall the strongest response to cloud seeding. In each subsequent step the algorithm mapped all grid cells adjacent to these three regions and added the one with the largest negative RFP value to the set of optimal grid cells. This procedure was continued until the selected regions covered 3.3% of the globe. This area coverage is equal to that used in theJones et al. [2009] study. The optimized regions are indicated with red lines in Figure 2. While the optimized regions in this study are quite similar to the those used by Jones et al. [2009], our regions in the South Pacific and South Atlantic do not extend as far off the coasts as the corresponding regions in their experiments and, on the other hand, our region off the coast of North America is somewhat larger.

image

Figure 2. The 10-year mean of radiative flux perturbation (Wm−2) in simulation with artificial sea salt emissions over all oceans (ALL). Red lines indicate regions selected as optimal by algorithm described in section 2.4.

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[21] Some points are worth noting: First, in the simulation ALL, which assumes geoengineering emissions over all oceans, the RFP of a specific region is not determined solely by the emissions in that region but is affected also by the transport of injected particles in and out of the region. Second, the optimization algorithm required that the emission masks for the three selected regions were continuous and did not contain gaps within the regions. Because of these two factors, the emission mask derived above may not be exactly optimized. However, the possible deviations are negligible and do not affect the conclusions of this study.

3. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

3.1. Budget of Injected Sea Salt Particles

[22] The mass emissions and the approximate burden of sea salt from both natural and artificial sources in the different simulations are presented in Table 2. Natural global sea salt emissions were approximately 5500 Tg yr−1 in all simulations with a variation of about 1% due to different wind conditions. Although the contribution of the artificial emissions to the total emitted sea spray mass was very small (0.4–7.4% depending on the simulation), they were responsible for 4–88% of the total number emissions of sea spray. As expected, multiplication of the baseline source function in simulations 3 × GEO and 5 × GEO resulted in a nearly linear increase in global mean emissions. For example in GEO the annual injected sea salt mass was 20.6 Tg yr−1 and in 3 × GEO 62.1 Tg yr−1. When geoengineering emissions were applied over all oceans (ALL), the sea salt burden was 27% higher than in the control simulation (CTRL); in all other geoengineering simulations the burden increased by less than 10%.

Table 2. The Simulated Budget of Sea Salt Particles From Both Natural and Artificial Sourcesa
SimulationNatural Emissions (Tg yr−1)Artificial Emissions (Tg yr−1)Burden (Tg)Burden Anomaly (Tg)
  • a

    Global total emissions and burdens are given in Tg. Burden anomaly is an estimate for the amount of sea salt in the atmosphere originating from artificial emissions.

CTRL5504.2014.1-
ALL5522.9443.917.93.8
GEO5470.120.614.30.2
3 × GEO5510.662.115.00.9
5 × GEO5467.3103.015.51.4
SMALL GEO5456.120.614.40.3
LARGE GEO5497.820.614.40.3

[23] Figure 3apresents the difference in total sea salt burden between the simulations GEO and CTRL, which is used as an estimate of the sea salt mass from artificial emissions. Note that since the ECHAM5.5-HAM2 model does not differentiate between sea spray particles from natural and artificial sources, the time-averaged global burden of the injected particles cannot be calculated exactly. In the baseline simulation GEO, the total sea salt burden was 0.2 Tg higher than the total sea salt burden of 14.1 Tg in the CTRL simulation (Table 2). This is an upper estimate for the sea salt burden from geoengineering injections since the life-time of the background particles was probably higher in the simulation GEO than in the simulation CTRL due to decreased scavenging by wet deposition. Within the geoengineered regions, the sea salt column burden from artificial sources was far from uniform and varied by roughly an order of magnitude (Figure 3a). Highest column burdens were found near the coast of South America and further away from the coasts of North America and Africa. The inhomogeneous distribution was caused primarily by variation in the wind speed dependent injection flux (Figure 3b) and transport of sea salt, but also by microphysical processes such as scavenging by wet deposition.

image

Figure 3. (a) The 10-year mean difference in total sea salt burden between the simulations GEO and CTRL and (b) the 10-year mean of sea salt number injections in the simulation GEO.

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[24] As can be assumed based on the small size of injected particles, they had a longer life-time than the natural sea spray particles on average. While the exact life-time of injected aerosol cannot be calculated as the model does not differentiate between particles from different sources, the life-time can be estimated by dividing the difference in sea salt burden between CTRL and geoengineering simulations with the total mass flux from artificial emissions. The mean life-time estimate of the injected sea salt particles was between 3.1 (ALL) and 5.2 days (3 × GEO) in the geoengineering simulations while the mean life-time of sea salt in simulation CTRL was only 0.9 days. The long life-time of the injected particles caused the relative increase in total sea salt burden to be higher than the relative increase in total sea salt emissions.

[25] It is also noteworthy that in our baseline case GEO, the mean number flux of artificial sea salt emissions was 57% of the flux assumed in the cloud-system-resolving model study ofWang et al. [2011]. In our simulations 3 × GEO and 5 × GEO the corresponding number flux was 73% and 187% higher than that in the study by Wang et al. [2011].

3.2. Effect of Injection Rate on Clouds

[26] Figure 4 presents the mean cloud top CDNC in the CTRL simulation without artificial sea spray emissions together with the relative CDNC increase in the geoengineering simulations GEO, 3 × GEO and 5 × GEO. Here the mean cloud droplet number concentration for each simulation was calculated by sampling only over cloudy time steps. In the marine clouds in CTRL simulation (Figure 4a), the mean CDNC at cloud top varied between 20 and 424 cm−3 with a mean of 100 cm−3.

image

Figure 4. The 10-year mean cloud droplet number concentration at cloud top: (a) absolute values in CTRL, and relative increase (with respect to CTRL) in (b) GEO (c) 3 × GEO and (d) 5 × GEO. Note that graphs have different scales.

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[27] The mean cloud top CDNC in the three geoengineered regions are given in Table 3. The simulated background CDNCs in the optimal geoengineering regions in the simulation CTRL (regional means 112–163 cm−3) are consistent with in situ measurements made in marine stratocumulus clouds off the Chilean coast during the VOCALS-Rex campaign which ranged between 80 and 400 cm−3 [Zheng et al., 2011]. On the other hand, the model-predicted CDNCs in the CTRL simulation tend to be somewhat higher than those from MODIS satellite retrievals [e.g.,Quaas et al., 2006]. The MODIS 5-year-mean CDNC (1 March 2000–28 February 2005) from retrieval byQuaas et al. [2006] for our North Pacific region was 84 cm−3 (versus 112 cm−3 in the model), for the South Pacific 93 cm−3 (versus 132 cm−3) and for the South Atlantic 97 cm−3 (versus 163 cm−3).

Table 3. Mean Values of Cloud Droplet Number Concentration (CDNC) at Cloud Top, Its Relative Change Compared to CTRL (ΔCDNC), Effective Radius of Cloud Droplets (reff) at Cloud Top and Liquid Water Path (LWP)a
 CTRLGEO3 × GEO5 × GEOSMALL GEOLARGE GEO
  • a

    Mean values are given for each optimal region and are sampled only over cloudy time steps.

North Pacific
CDNC (cm−3)112194407596978113
ΔCDNC (%)-742644347761
reff (μm)13.512.211.311.111.013.5
LWP (g m−2)99151219261296103
 
South Pacific
CDNC (cm−3)1322364576501016139
ΔCDNC (%)-802483946736
reff (μm)12.311.410.810.610.712.2
LWP (g m−2)107154225253287111
 
South Atlantic
CDNC (cm−3)1632865597841201193
ΔCDNC (%)-7524238063518
reff (μm)12.811.811.211.111.212.6
LWP (g m−2)7512018722924781

[28] There are several possible explanations for the difference in CDNC between model simulations and satellite measurements. First, the simulated aerosol fields from which the CDNC are calculated online may be too high. Unfortunately, aerosol data from satellites and in situ measurements are insufficient for a conclusive comparison of the modeled aerosol concentrations against observations. In the regions where the modeled aerosol concentrations were too high in the CTRL simulation, we would be likely to underestimate the efficiency of sea spray cloud seeding. Second, the simulated mean updraft velocities in the optimized regions were very high (1.0–1.4 m s−1). In field measurements, typical updraft velocities range between 0.2 and 0.4 m s−1, although much higher as well as lower values have also been measured [Lu et al., 2009; Meskhidze at al., 2005]. In the light of these measurements, the updraft velocities in ECHAM5.5-HAM2 may lead to overestimation of activated cloud droplets. This model feature can causeunderestimation of cloud seeding efficiency since it is likely to overestimate the CDNC from background particles more than that from the injected particles (which are mostly large enough to activate at any reasonable updraft). Third, CDNCs retrieved from remote sensing observations suffer from many uncertainties. CDNC retrievals are based on the cloud optical thickness (COT) and cloud droplet effective radius reff which are lower order cloud properties retrieved from remote sensing observation. CDNC retrieval is especially sensitive to changes in reff, for which the values differ significantly between different retrieval algorithms and remote sensing instruments [e.g., Bennartz, 2007; Breon and Doutriaux-Boucher, 2005; Maddux et al., 2010] translating to large uncertainty in CDNC.

[29] Figures 4b–4d show that in the simulation GEO, the artificial sea salt injections changed the cloud droplet concentrations substantially but with a high spatial variation. The highest relative increases in CDNC were situated close to the coastline. For example, close to the California coast in the North Pacific region, the relative increase was in the range of 90–150% while further to the ocean it was only in the range of 30–70%. The likely reason for the strong response near the coasts is discussed in later in this section. Although the relative increases of cloud top CDNC were higher in 3 × GEO and 5 × GEO, the spatial pattern of increase was similar in all three geoengineering simulations. It is noteworthy that the effect of artificial sea salt emissions extended also outside the emission regions, and in some areas as far as 1500 km away from emission regions the CDNC at cloud top increased by 10% in the simulation GEO.

[30] Table 3 summarizes the mean relative changes in cloud top CDNC in the three optimized regions. In simulation GEO, the changes in CDNC for North Pacific, South Pacific and South Atlantic regions were 74, 80 and 75%, respectively. It could be expected that the increase in CDNC would be sublinear with respect to the magnitude of the emission flux. However, in our simulations the increase of CDNC followed the multiple of the baseline mass flux superlinearly: averaged over all optimized regions, CDNC increased in GEO by 75%, in 3 × GEO by 253% and in 5 × GEO by 408%. There are several reasons for this superlinearity. First, the particle concentration in the soluble accumulation mode increased superlinearly. This was probably due to weakened scavenging by wet deposition as the mean precipitation decreased in the emission regions by 2–5% in most simulations (second indirect effect). Second, vertical velocities were higher with higher artificial sea salt fluxes. Third, due to the modal aerosol description, the mean diameter of the accumulation mode increased more (compared to CTRL) in 3 × GEO and in 5 × GEO than in GEO, which makes activation to cloud droplets more probable.

[31] When assuming the baseline flux GEO, the mean absolute values of CDNC in the emission regions remained below 375 cm−3 (Table 3), which is a value assumed in several previous climate model studies of sea spray geoengineering [Latham et al., 2008; Jones et al., 2009]. However, in simulation 3 × GEO the mean CDNC at cloud top over all emission regions was 458 cm−3 and in 5 × GEO it was 658 cm−3. Even these values are significantly lower than the 1000 cm−3 assumed by Rasch et al. [2009].

[32] The strong response in CDNC in the three optimized regions can be explained by their relatively low background CDNC (regional mean values in CTRL 112–163 cm−3, Table 3) and the stratocumulus clouds that reside at heights of 100–500 m (Figures 5d–5f). Since the strongest effect of sea spray injections was limited to the lowest ∼2 km (Figures 5a–5c), these low-lying clouds are an ideal target for cloud whitening. These low clouds also occur frequently, which explains the strong radiative flux perturbation in these regions when all oceanic regions are geoengineered (simulation ALL,Figure 2). Note, however, that frequent cloud occurrence was not the cause of the high mean absolute CDNC values in these regions as only cloudy time steps were considered.

image

Figure 5. (a–c) 10-year mean regional mean cloud droplet number concentration at different altitudes in three emission regions. Mean values are sampled only over cloudy time steps. (d–f) Regional mean cloud cover profiles for three emission regions. Note that profiles of the simulations CTRL and LARGE GEO almost overlap.

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[33] Our study shows a much stronger cloud response to sea spray injections than the previously published study by Korhonen et al. [2010] who assumed similar number injection fluxes (increase in CDNC is mainly dependent on number instead of mass flux, provided that injected particles are large enough). Korhonen et al. [2010] simulated a regional mean increase of only 20% or less with a flux comparable to GEO (mean in this study: 75%) and of 163% or less with a flux comparable to 5 × GEO (mean in this study: 408%). Several factors may contribute to this difference.

[34] First, the regional mean background CDNCs in the control simulation without geoengineering were somewhat higher in the previous study (143–177 cm−3) compared to this study (112–163 cm−3).

[35] Second, Korhonen et al. [2010]used a chemical transport model with prescribed meteorology and could therefore not include cloud feedbacks on the aerosol concentration. In our study these feedbacks are simulated, and they increase the life-time of the injected particles and thus the change in the CDNC. The increase in particle life-time is evident e.g., from the superlinear increase in the particle number concentration in the soluble accumulation mode when the magnitude of the artificial sea salt emissions is increased (not shown).

[36] Third, the reference altitudes for CDNC in these two model studies were not exactly comparable. While Korhonen et al. [2010]calculated their CDNC fields offline at an approximate cloud base altitude of 1 km, ECHAM5.5-HAM2 indicates that stratocumulus clouds in the seeding regions are frequently found at 100–500 m altitude (Figure 5). We recalculated the CDNC fields using the model results of Korhonen et al. [2010] at 360 m altitude and found that the relative CDNC change increased by only up to 5 percentage points in the simulation corresponding to GEO and by up to 40 percentage points in the simulation corresponding to 5 × GEO. Thus, the differences in cloud altitude alone cannot explain the differences in CDNC enhancements between the two studies.

[37] Fourth, Korhonen et al. [2010]simulated a significant suppression of supersaturation when the artificial emissions were present. This meant that some of the smaller background particles forming cloud drops in their control simulation did not activate to droplets in the geoengineering simulations, thus reducing the relative CDNC increase. This effect is lacking in our study, since ECHAM5.5-HAM2 simulates much higher effective updrafts (1.0–1.4 m s−1) than assumed by Korhonen et al. [2010] (Gaussian distribution with mean of 0 m s−1 and standard deviation of 0.25 m s−1) and thus the small background particles that activated in our simulation CTRL, activated also in the geoengineering simulations. Since the updraft velocities assumed by Korhonen et al. [2010] are lower than in typical measurements [Lu et al., 2009; Meskhidze at al., 2005], it is possible that Korhonen et al. [2010] overestimated the supersaturation suppression effect. However, recalculating their CDNC fields using a significantly higher updraft of 0.8 m s−1 increased the predicted relative CDNC changes by only up to 13 percentage points in the simulation corresponding to GEO and decreased the CDNC change in the simulation corresponding to 5 × GEO (because at high updraft smaller background particles activated to droplets). Therefore, the differences in updraft velocities alone cannot explain the differences in CDNC enhancements between the two studies.

[38] Another difference between this study and that of Korhonen et al. [2010] is that they found the maximum increase in relative CDNC further off the coast over the open ocean. Korhonen et al. [2010] explained their result with the high background concentration of anthropogenic aerosols buffering the relative CDNC change close to the continents. The anthropogenic effect on aerosol fields is also visible in our simulations and thus the difference between the two studies cannot be explained by different emissions.

[39] The most likely reason for this discrepancy has to do with the structure of a modal aerosol description such as HAM2. Due to anthropogenic pollution, the dry geometric mean diameter of the soluble accumulation mode in the control simulation (CTRL) was smaller in the nearest 500 km from the coasts than further out over the ocean but within the geoengineered regions (approximately 140–160 nm and 180–210 nm, respectively) and the organic fraction is higher close to the continents. Because the injected particles have a dry diameter of 250 nm and a higher solubility than the background particles, the mean diameter of the accumulation mode as well as the particle solubility increased close to the continents in the geoengineering simulations relative to the CTRL simulation. Since the standard deviations of the modes are fixed in HAM2, this means that the size and solubility of the background accumulation mode particles is somewhat overestimated. Because of this, some of the background particles that did not activate in the CTRL simulation form cloud droplets in the geoengineering simulations. As a result, modal aerosol models such as ECHAM5.5-HAM2 tend to overestimate the CDNC in geoengineering simulations in anthropogenically influenced regions and thus our results on cloud seeding efficiency in these regions are likely to be an upper estimate. It should be noted thatKorhonen et al. [2010] used a sectional aerosol model in their study and thus did not suffer from this effect.

[40] Figure 6a shows that artificial sea salt emissions caused also an increase in cloudiness (second indirect effect). For example, in the simulation GEO the mean total cloud cover over the North Pacific region increased from 59.4% to 63.3%, over the South Pacific from 59.3% to 61.7% and over the South Atlantic from 46.4% to 50.8%. The areas with the strongest increase in total cloud cover were roughly the same ones which had the highest increase in CDNC (Figures 4b–4d) and liquid water path (LWP) (not shown). The mean LWP in the optimized regions increased by 44–61% in GEO compared to CTRL (Table 3). It should be noted, however, that modeling the second indirect effect with global climate models has still many uncertainties. For example, compared to satellite observations, climate models tend to overestimate the effect of aerosol number concentration increase on the LWP, and underestimate the correlation between aerosol optical depth and cloud fraction [Quaas et al. 2009].

image

Figure 6. (a) Change of total cloud cover (percentage points) and (b) relative change (%) of cloud droplet effective radius at cloud top in GEO compared to the simulation CTRL. The 10-year mean values for total cloud cover and cloud droplet effective radius were used.

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[41] The first indirect effect, i.e., the decrease in effective radii of the droplets when their number concentration increases, was also evident in all simulations (Figure 6b and Table 3). In GEO, the decrease in cloud top effective radius was 9% when all seeding regions were considered. As an example, in the North Pacific region the mean effective radius was 13.5 μm in CTRL and 12.0 μm in GEO. Higher injection rates further decreased the effective radii of the cloud droplets: in 3 × GEO the mean decrease over the three emission regions was 15% and in 5 × GEO 16%. However, there was significant spatial variation within and between the emission regions (Figure 6b). The strongest effect was found in the North Pacific region where in simulation 5 × GEO the mean decrease in cloud top effective radius was over 4 percentage points higher compared to the other two regions.

3.3. Effect of Particle Injection Size on Clouds

[42] If the mass flux of seawater sprayed from the vessels is limited by technological constraints, the size of the injected aerosol particles plays an important role. In order to investigate the effect of injection size on the cloud seeding efficiency, we conducted two additional simulations with different geometric mean diameters for the injected particles: in simulation SMALL GEO, the diameter was set to 100 nm, and in simulation LARGE GEO to 500 nm. Note that the total mass flux in both of these simulations was the same as in GEO, and thus the number flux increased by 1460% in SMALL GEO and decreased by 88% in LARGE GEO compared to the simulation GEO.

[43] Despite the small size of the injected particles in the simulation SMALL GEO, most of them activated to cloud droplets leading to a mean cloud top CDNC of 1046 cm−3 and a mean relative increase of 707% in the three emission regions. This relative increase was notably higher than the CDNC increase in GEO (75%) or even in 5 × GEO (408%), although it did not follow the magnitude of the number flux linearly (number flux in SMALL GEO was 1460% higher than in GEO) as it did in simulations GEO, 3 × GEO and 5 × GEO. The high efficiency of SMALL GEO can be seen also in Figures 5a–5c, which show that CDNC was about 1500 cm−3 at altitudes typical for stratocumulus clouds.

[44] These results indicate that decreasing the particle injection size can in many situations be a much more effective way to improve the cloud seeding efficiency than increasing the seawater mass flux from the spraying vessels. It must be remembered, however, that the injection size cannot be decreased much below 100 nm if one wants to be sure that the particles activate to cloud droplets in typical stratocumulus updrafts. It should be also noted, that the large updraft velocities in ECHAM5.5-HAM2 may bias the number of activated droplets high in the SMALL GEO run in which the injected particles are small.

[45] Table 3 shows that the changes in cloud droplet effective radius and LWP were similar in SMALL GEO and 5 × GEO. The regional mean effective radii at cloud top in SMALL GEO were 10.7–11.2 μm, which is 1.6–2.5 μm smaller than in the CTRL simulation. On the other hand, the regional mean LWP in SMALL GEO varied between 247 g m−2 and 296 g m−2 while the corresponding values for CTRL were between 75 g m−2 and 107 g m−2.

[46] Increasing the geometric mean diameter to 500 nm in the simulation LARGE GEO decreased the artificial sea salt number flux by 88% compared with that in GEO. As expected, this decrease was seen in the much lower CDNC increase compared to other geoengineering simulations. Cloud top CDNC increased by only 18% in the South Atlantic region and even less in the other two emission regions (Table 3). Correspondingly, the mean vertical profiles of CDNC in LARGE GEO (Figures 5a–5c) were almost identical to profiles in CTRL. Changes in effective radius and LWP were also almost negligible in LARGE GEO compared to the CTRL simulation (Table 3).

3.4. Radiative Effects

[47] The global and regional mean values of radiative flux perturbation (RFP), aerosol direct effect (difference in aerosol direct forcing of all atmospheric particles between a geoengineering and the control simulation) and aerosol indirect effect (the difference of RFP and the direct effect) for all the simulations are summarized in Table 4. The calculation of direct and indirect effects is explained in more detail in section 2.4. While the direct and indirect effects cannot be unambiguously separated, the chosen method can be used to investigate the approximate relative contributions of aerosol direct and indirect effects to the total radiative effects.

Table 4. Global and Regional Mean Values of the Radiative Effectsa
 ALLGEO3 × GEO5 × GEOSMALL GEOLARGE GEO
  • a

    For each geoengineering simulation radiative flux perturbation (RFP), aerosol direct effect and the aerosol indirect effect are given. Values are calculated using net total (SW + LW) radiation at the top of the atmosphere.

North Pacific
RFP (Wm−2)−18.0−15.6−29.9−37.8−35.9−2.0
Direct (Wm−2)−1.5−1.2−3.2−4.9−0.6−1.1
Indirect (Wm−2)−16.5−14.5−26.7−32.8−35.3−0.9
 
South Pacific
RFP (Wm−2)−22.2−16.3−34.5−40.6−40.3−2.5
Direct (Wm−2)−2.1−1.1−3.2−5.2−0.4−1.5
Indirect (Wm−2)−20.1−15.2−31.3−35.4−39.9−1.1
 
South Atlantic
RFP (Wm−2)−21.8−14.8−30.9−40.5−36.0−2.7
Direct (Wm−2)−1.6−0.8−2.8−4.70.7−1.4
Indirect (Wm−2)−20.2−14.0−28.1−35.9−36.6−1.3
 
Global Mean
RFP (Wm−2)−5.1−0.8−1.7−2.2−2.1−0.2
Direct (Wm−2)−1.5−0.1−0.3−0.5−0.1−0.1
Indirect (Wm−2)−3.6−0.7−1.4−1.7−2.1−0.1

[48] When the artificial sea salt emissions were limited only to the three optimized regions, the strongest RFP was achieved in the 5 × GEO simulation (global mean RFP −2.2 Wm−2) followed by SMALL GEO (−2.1 Wm−2). This is slightly surprising because the relative increase of the mean CDNC over the optimal regions was clearly higher in the simulation SMALL GEO (707% versus 408% in 5 × GEO). This apparent discrepancy is due to the much larger direct aerosol effect in the 5 × GEO simulation (−0.5 Wm−2 versus −0.1 Wm−2 in SMALL GEO) and was mainly caused by the larger mass flux in the simulation 5 × GEO. Furthermore, the indirect effects started to saturate at high CDNC values.

[49] As expected, the regional radiative effects were much stronger than the global mean effects in all our simulations. For example, in simulation SMALL GEO (global mean RFP −2.1 Wm−2) the regional means were −35.9 Wm−2, −40.3 Wm−2 and −36.0 Wm−2 for the optimized North Pacific, South Pacific and South Atlantic regions, respectively. It is noteworthy that such high local forcings could have significant impacts on the atmospheric dynamics as well as on local marine ecosystems.

[50] When the sea spray injections were limited to the three optimized regions, none of our simulations could produce high enough RFP to counteract the doubling of carbon dioxide concentrations from the pre-industrial era (estimated forcing +3.7 Wm−2 [Forster et al., 2007]). However, when the injections were extended over all oceans (ALL), the global mean RFP was −5.1 Wm−2 which would be more than enough to compensate for the CO2 doubling.

[51] Our results can be compared against those of Jones et al. [2009], who modified clouds over an area equivalent to our optimized areas (3.3% of Earth's surface) and obtained a global mean RFP of −0.97 Wm−2. This is comparable to the RFP in our GEO simulation (−0.8 W m−2). Our GEO simulation predicts clearly lower CDNC (regional means varied between 194 and 286 cm−3) than the 375 cm−3 assumed by e.g., Jones et al. [2009]but an increase in cloud cover by 2–5 percentage points. Furthermore, we predict also a non-negligible direct effect (−0.1 W m−2) which was omitted by Jones et al. [2009]. Thus, in our study the aerosol direct effect compensated to some extent for the lower indirect effects.

[52] In all the simulations with injections restricted to the three optimal regions (apart from the run LARGE GEO), the aerosol indirect effects dominated over the aerosol direct effects. The extreme example was SMALL GEO, for which the global mean direct effect was only −0.1 Wm−2 compared to the indirect effect of −2.1 Wm−2. However, in most simulations the absolute values of the regional mean direct effect were quite significant. For example, in simulation GEO they were −1.2, −1.1 and −0.8 Wm−2 for the North Pacific, South Pacific and South Atlantic regions, respectively. However, these values are still much lower than the corresponding regional mean values of the indirect effect (−14.5, −15.2 and −14.0 Wm−2, respectively). It is also worth noting that the global mean direct effect depended nearly linearly on the mass flux of artificial sea salt emissions, as in simulation 3 × GEO it was 3.1 times and in simulation 5 × GEO 4.6 times that of GEO. On the other hand, the indirect effect was clearly sublinear, being −0.7 Wm−2 in GEO and −1.7 Wm−2, i.e., only 1,4 times larger, in 5 × GEO.

[53] Note that for the South Atlantic region the calculated mean direct effect in the simulation SMALL GEO was +0.7 Wm−2 (Table 4). This positive value is an example of how changes in cloud cover can affect the direct effect calculated by the difference of total aerosol forcing between two different simulations (see section 2.4). In this region, the aerosol optical depth (AOD) was 87% higher in the simulation SMALL GEO compared to CTRL. Furthermore, the direct effect using the clear-sky values was −4.2 Wm−2, which shows that the artificial sea salt emissions had a cooling effect, although the calculated total-sky direct effect was positive due to highly increased cloud cover.

[54] Figure 7 shows the geographical distributions of the direct and indirect effects for the simulations ALL and GEO. Areas with the strongest direct and indirect effect do not overlap since in regions with persistent cloud cover the aerosol direct effects are of minor importance. For example, on the coast of Africa the strongest direct effect was found on the western edge of the emission region, but the strongest indirect effect next to the coast where low clouds occur more frequently. Generally, the geographical distribution of the direct effect in the simulation GEO (Figure 7b) was similar to the estimate for the burden of sea salt originating from artificial emissions (Figure 3b).

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Figure 7. The 10-year mean radiative effects in the simulations ALL and GEO. (a) The aerosol direct effect in the simulation ALL and (b) the aerosol direct effect in the simulation GEO. (c) The aerosol indirect effect calculated as the difference between radiative flux perturbation (RFP) and the aerosol direct effect in the simulation ALL and (d) the corresponding aerosol indirect effect for the simulation GEO. Note that direct and indirect effects have different scales.

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[55] Unlike in the other simulations, in simulation ALL the aerosol direct effect was a significant part of the total radiative effect. The global mean direct effect was −1.5 Wm−2 while the indirect effect was −3.6 Wm−2. Between latitudes 16°N and 5°S, the direct effect was even stronger than the indirect effect: the zonally averaged direct effect over ocean was about −3 Wm−2 compared to the indirect effect of about −2 Wm−2. There are two main reasons for this high relative importance of direct effect in ALL compared to the other simulations. First, large parts of the ocean area have either a low total cloud cover or only few low-altitude clouds (e.g., close to the equator) that can be substantially affected by the sea salt injections. In these regions, the simulated aerosol indirect effects were fairly unimportant. Second, in the other simulations all the emission areas were highly clouded and thereby the aerosol direct effect was relatively unimportant compared to aerosol indirect effects. In the simulation ALL, these stratocumulus regions covered only a small fraction of the total emission area.

3.5. Effects in the Arctic

[56] While the three persistent stratocumulus regions off the west coasts of North and South America and South-Western Africa are likely to be the most favorable to sea spray geoengineering in terms of global radiative forcing, the method could also be used to target specific regions that are likely to face abrupt climate change in the future. One such region is the Arctic where especially the summer-time sea ice is in danger to melt due to global warming [Boé et al., 2009]. Since we used climatological sea surface temperature and sea ice fields, we could not calculate the actual cooling effect in the Arctic due to sea spray geoengineering; however, our simulation ALL indicates that if operated in the Arctic region, the sea spray vessels could produce a significant local negative forcing in the summer time.

[57] In the polar regions the simulated natural background aerosol consisted mainly of small particles and only a small fraction of them activated to cloud droplets. Thus when the polar oceans were seeded in simulation ALL, the injected sea salt particles dominated the cloud condensation nuclei (CCN) numbers and CDNC increased significantly in summer time. This resulted in a summer (JJA) mean RFP of −2.1 Wm−2 over the Arctic (60°N–90°N) (Figure 8). While the forcing effect did not extend far over the ice sheet, it might be able to cool at least the most vulnerable ice sheet edge regions sufficiently [Holland et al., 2006].

image

Figure 8. The mean summertime (JJA) radiative flux perturbation in the Arctic in the simulation ALL.

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3.6. Sensitivity of the Results to Ultrafine Sea Salt Emissions

[58] As discussed in section 2.3, one limitation with the ECHAM5.5-HAM2 model is that it does not include natural ultrafine sea spray emissions (dp < 100 nm) which can contribute significantly to cloud condensation nuclei especially in remote marine areas [Pierce and Adams, 2006]. It is therefore possible that our model underestimates the background aerosol concentration and thus overestimates the relative CDNC increase and radiative forcing in the geoengineering simulations. In order to estimate the sensitivity of our results to the lack of ultrafine sea spray emissions, we made two additional simulations (UF-SS CTRL and UF-SS ALL), which included a simplified implementation ofMårtensson et al. [2003] parameterization for ultrafine sea salt emissions (described in section 2.3.2) but were in other respects identical to simulations CTRL and ALL, respectively.

[59] With the ultrafine sea salt emissions, the regional mean cloud top CDNCs in simulation UF-SS CTRL were 128, 153 and 198 cm−3for the optimized regions in the North Pacific, South Pacific and South Atlantic, respectively. These values are on the average about 20% higher than in simulation CTRL. The geographical pattern of the relative difference between UF-SS CTRL and CTRL is similar to increase of CDNC due to geoengineering. The strongest enhancement in background CDNC is seen in the polar regions and in the three stratocumulus regions where low-altitude clouds are abundant.

[60] Figures 9a and 9bshow the relative increase of cloud top CDNC in simulations ALL (with respect to CTRL) and UF-SS ALL (with respect to UF-SS CTRL).Figure 9c shows the difference in the percentage change of CDNC due to geoengineering between the simulation with ultrafine sea spray included and the corresponding simulation with ultrafine sea spray not taken into account as described by the following formula:

  • display math

where UF-SS denotes that natural ultrafine sea salt emissions were included in the simulation. The measure of this quantity is percentage points. Thus,Figure 9c shows how much the ultrafine sea salt emissions affect the efficiency of cloud modification. In Figure 9c, negative values indicate that introducing ultra-fine sea spray decreases the efficiency of geoengineering and positive values that ultra-fine sea spray enhances the effects of geoengineering.

image

Figure 9. The effect of ultrafine sea salt emissions on cloud droplet number concentration (CDNC) at cloud top, when artificial sea salt emissions are placed over all ocean area. (a) Relative change (%) between geoengineering simulation without natural ultrafine sea salt emissions (ALL) and control simulation (CTRL). (b) Relative change (%) between geoengineering simulation with natural ultrafine sea salt emissions (UF-SS ALL) and control simulation with natural ultrafine sea salt emissions (UF-SS CTRL). (c) The sensitivity of CDNC change to natural ultrafine sea salt emissions (equation (3)). The unit in Figure 9c is percentage points. CDNC was sampled over cloudy time steps over the 10-year simulation time.

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[61] By and large the CDNC enhancements in the simulations with and without ultrafine sea salt emissions look very similar (Figures 9a and 9b). For the majority of the globe, the sensitivity of CDNC change to ultrafine emissions was less than 15 percentage points (Figure 9c). However, some exceptions exist. For example, close to the South Pole (55–62 °S) the difference between the two set-ups was quite large with zonally averaged CDNC enhancement about 20–40 percentage points lower when ultrafine sea salt was included. This is probably due to the very low natural CDNC in the Southern Ocean: as the natural background concentration was smaller in CTRL than in UF-SS CTRL, a high relative increase was also easier to achieve in the former simulation.

[62] In the three optimized geoengineering regions, the CDNC change was not highly sensitive to ultrafine sea salt. The largest effect was found in the South Atlantic region where the increase in CDNC was 20 percentage points smaller in the simulation with ultrafine sea salt emissions. In the North and South Pacific, the changes in CDNC enhancement were only −3 and −5 percentage points, respectively. Compared to the total CDNC enhancement in geoengineering simulations (regional means 84–105% in simulation ALL), these changes are not very significant.

[63] However, even these relatively small changes in CDNC had a notable effect on the radiative flux perturbation. Global mean RFP in UF-SS ALL (compared to UF-SS CTRL) was −4.5 Wm−2 while it was −5.1 Wm−2in ALL (compared to CTRL). The regional difference in RFP between UF-SS and standard simulations was largest in the South Atlantic region (5.7 Wm−2), where the CDNC increase due to geoengineering was strongest. On the other hand, the ultrafine sea salt emissions had almost negligible effect on aerosol direct forcing due to their small size. In the Arctic (60°N–90°N) the addition of ultrafine sea salt emissions had little effect on summertime RFP. The summertime (JJA) mean RFP was −2.1 Wm−2in both ALL and UF-SS ALL.

[64] All in all, these sensitivity simulations indicate that our results presented in previous sections are not highly sensitive to excluding natural ultrafine sea salt emissions. One possible exception is the polar regions which have low background aerosol concentrations in the accumulation mode.

4. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[65] In this study we have simulated geoengineering via sea spray injections with a state-of-the-art aerosol-climate model ECHAM5.5-HAM2. With explicit online calculation of cloud droplet activation, aerosol-cloud interactions and radiation, we were able for the first time to estimate the relative importance of direct and indirect aerosol effects of this geoengineering scheme. We performed one simulation with artificial sea salt injections over all oceans and used this simulation to identify regions most susceptible to cloud whitening. We then made several simulations in which the injections were confined to three optimal stratocumulus regions in the North Pacific, South Pacific and South Atlantic (covering 3.3% of the Earth's surface) and the total mass flux as well as the size of the injected particles were varied.

[66] Aerosol direct effect (scattering of solar and terrestrial radiation), which has been omitted in previous studies, was an important part of the total radiative effect outside heavily clouded regions. When all oceanic areas were geoengineered, the global mean direct forcing was −1.5 Wm−2 compared to the indirect effect of −3.6 Wm−2. While the indirect effects dominated over the direct effects in the three persistent stratocumulus regions, our results imply that the method may cause non-negligible cooling also in the case the injected sea spray is transported to clear-sky regions.

[67] Our simulations indicate that decreasing the size (and increasing the number flux proportionally) of injected particles can be an efficient way to raise the efficacy of sea spray geoengineering (as long as the particles are still large enough to activate as cloud droplets). Within the three optimal regions, a comparable enhancement in the cooling efficiency was obtained by multiplying the baseline mass flux by 5 and by reducing the injected particle mean diameter from 250 nm to 100 nm. However, such small particles are efficient only in terms of indirect, but not direct, radiative effects. On the other hand, it is possible that the resulting high CDNCs reduce drizzle formation and thus increase the lifetime of and direct aerosol effect from larger accumulation mode particles. It should be noted, however, that the very high updraft velocities predicted by the ECHAM5.5-HAM2 model (1.0–1.4 m s−1) may overestimate the number of small particles that get activated and thus overestimate the cooling effect when the particle injection size is decreased.

[68] Based on our simulations, geoengineering in only the three optimal stratocumulus regions is not enough to compensate for the forcing of +3.7 Wm−2associated with the doubling of carbon dioxide concentrations since the pre-industrial era [Forster et al., 2007]. With the baseline flux and injected particle mean diameter of 250 nm, a radiative flux perturbation (RFP) of −0.8 Wm−2 was achieved. This is comparable to −0.97 Wm−2 obtained by Jones et al. [2009] who used the same total area for cloud modification. However, if it were possible to apply geoengineering over all oceans, sufficient cooling effect to counteract CO2 doubling might be achieved. In the simulation with sea salt injections over all oceans, the global mean RFP was −5.1 Wm−2.

[69] In terms of injected sea salt mass, it could be technically feasible to produce the simulated baseline flux over the three optimized regions (20.6 Tg yr−1) with the vessel design by Salter et al. [2008]. With an injection rate of seawater per ship of 30 kg s−1, which corresponds roughly to sea salt injections of 1 kg s−1, the total mass flux could be achieved by less than 1000 ships assuming that they operated at full power at all times (i.e., surface wind speed >7 m s−1). However, achieving spatially nearly homogeneous emissions over 3.3% of the Earth's surface (as assumed in global climate model simulations) with such a small number of sea spraying vessels would probably prove very difficult [Wang et al., 2011]. Furthermore, counteracting CO2 doubling would require a much higher sea salt total mass flux and thus multiply the number of needed vessels at least by a factor of about 10.

[70] Although several studies have demonstrated that increasing CDNC in marine stratocumulus regions could substantially counteract the rise of the global mean temperature [Bala et al., 2010; Jones et al., 2009; Latham et al., 2008; Rasch et al., 2009], our study further builds up the conclusion that modeling of sea spray geoengineering has significant uncertainties especially in aerosol-cloud interactions due to different model formulations and variations in e.g., background aerosol concentration, updraft velocity and cloud altitude. This is demonstrated by a considerably larger increase of CDNC in our study than in simulations byKorhonen et al. [2010]who used comparable source functions for sea spray number injections. Another uncertainty is that global models cannot properly capture all relevant sub-grid cloud processes [Lee et al., 2009]. On the other hand, cloud-resolving models cannot assess global effects of cloud modification.

[71] If sea salt could be injected at a rate that is sufficiently homogeneous and high, the simulated effects on clouds and Earth's radiation balance indicate that this technique might be sufficiently potent to be used at least as a part of geoengineering option. It should also be stressed that we focused only on changes in cloud properties and radiation balance, and thus our simulations did not address any changes in the hydrological cycle [Bala et al., 2010] or possible other inadvertent side-effects geoengineering might have. Because of these risks, and the fact that any solar radiation management technology could possibly be needed for millennia [Brovkin at al., 2009] geoengineering should be considered only as a countermeasure for abrupt climate change or for a serious threat of e.g., accelerated melting of Greenland ice sheet [Christoffersen and Hambrey, 2006] or collapse of the West Antarctic ice sheet [Joughin and Alley, 2011] and not as a substitute for urgent reduction of greenhouse gas emissions.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[72] This work was supported by Maj and Tor Nessling foundation under grant 2011072, the Academy of Finland's Research Program on Climate Change (FICCA) (project 140867) and Academy of Finland's project 123466.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
jgrd17477-sup-0001-t01.txtplain text document1KTab-delimited Table 1.
jgrd17477-sup-0002-t02.txtplain text document1KTab-delimited Table 2.
jgrd17477-sup-0003-t03.txtplain text document1KTab-delimited Table 3.
jgrd17477-sup-0004-t04.txtplain text document1KTab-delimited Table 4.

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