3.1. Radiative Flux Perturbations
 The RFP (“forcing”) caused by modifying CDNC in the different stratocumulus regions is given in Table 1, along with the percentage area of the Earth covered by each region. It is immediately apparent that the RFP produced is of a significant magnitude: modifying all three areas produces an RFP of almost −1 Wm−2, which would offset roughly 35% of the forcing due to current levels of anthropogenic greenhouse gases [IPCC, 2007]. An important difference between the RFP caused by this geoengineering approach and the forcing due to well-mixed greenhouse gases is that the RFP is highly nonuniform, with the majority of the RFP being localized in the modified region. A simple measure of this is the hemispheric RFP shown in Table 1. For the NP case, the ratio between northern and southern hemisphere forcing is a little under 4 to 1, whereas the hemispheric balance is reversed in ALL, with the ratio being approximately 1 to 5 (NH to SH). In the SP and SA cases the RFP is almost totally in the southern hemisphere.
Table 1. Annual Mean Radiative Flux Perturbation Caused by Modifying CDNC in Various Stratocumulus Regions (W m−2, ±1SD)a
|Area Modified||Global||NH||SH||Sc||RoW||Area (%)|
|NP||−0.45 ± 0.08||−0.70 ± 0.11||−0.19 ± 0.08||−0.26 ± 0.01||−0.18 ± 0.08||0.7|
|SP||−0.52 ± 0.09||−0.04 ± 0.15||−1.00 ± 0.08||−0.45 ± 0.01||−0.07 ± 0.09||1.5|
|SA||−0.34 ± 0.09||+0.02 ± 0.14||−0.71 ± 0.09||−0.34 ± 0.01||−0.003 ± 0.09||1.1|
|ALL||−0.97 ± 0.09||−0.32 ± 0.16||−1.62 ± 0.06||−1.06 ± 0.02||+0.09 ± 0.09||3.3|
 Examination of the global mean RFP values in Table 1 shows that the RFPs are not additive: the sum of the three regions considered separately is −1.31 Wm−2, 35% larger than the RFP produced when all three regions are modified simultaneously. The contribution to the global mean RFP of the modified and unmodified regions in Table 1 shows that the unmodified “rest-of-world” contributes approximately 40% of the RFP in the NP case, almost nothing in SA, and a small counteracting positive RFP in the ALL case. RFP in a given modified area is approximately the same in the ALL simulation as in the corresponding individual-area simulation, indicating that the RFP in a given (modified) stratocumulus region does not depend on whether other stratocumulus regions are being modified. The lack of additivity for RFP may therefore be ascribed to the RFP induced in unmodified areas by the change in climate caused by the geoengineering of the various stratocumulus regions. This also helps explain why the global mean RFPs do not scale with the area of the modified regions (Table 1).
3.2. Climate Sensitivity and Efficacy
 Table 2 shows the climate sensitivity (λ, defined as degrees K of near-surface temperature change per Wm−2 of RFP) and climate efficacy (λ divided by λ for CO2 [Hansen et al., 2005]) from the HadGEM2-AML experiments where each stratocumulus area was modified separately, as well as the ALL case for comparison. The RFP values used to compute these sensitivities are those from the HadGEM2-A simulations presented in Table 1. Previous experiments with this model suggest that climate sensitivity is around 1 K W−1 m2 for increases in carbon dioxide [Jones et al., 2007], which is not greatly different to the sensitivity found for the NP or SA cases, i.e. their climate efficacy is close to 100%. However, it is clear that global mean temperature is much more sensitive to perturbations in the South Pacific, which has a climate efficacy more than twice that associated with the other two regions. If only one marine stratocumulus area could be targeted for geoengineering, the larger climate efficacy in the SP case might suggest this as an appropriate candidate. However, it is important to consider the continental and regional scale responses to such localized geoengineering.
Table 2. Climate Sensitivity (λ) and Climate Efficacy (%: λ / λ for CO2) for Modified Marine Stratocumulus Areas as Determined From HadGEM2-AML Simulations for the Near-Surface Equilibrium Temperature Change (K) and HadGEM2-A Simulations for Radiative Flux Perturbation (Wm−2; see Table 1)a
|Area Modified||λ (K W−1 m2)||Efficacy (%)|
3.3. Climate Response
 Figure 2 shows the evolution of global mean near-surface air temperature from 2000 to 2060 in the A1B control and ALL geoengineering experiment of the coupled HadGEM2-AO model, in terms of the anomaly with respect to the mean 1860 value. Both A1B and ALL simulations start in the year 2000 at about 0.8 K warmer than the 1860 mean. Thereafter A1B warms by approximately a further 1.7 K by 2060, although there are some periods of little warming (e.g., 2000 to 2010 and 2050 to 2060). Applying the geoengineering modification to all three stratocumulus areas from 2000 in ALL causes the near-surface air temperature to fall rapidly (within 5–10 years) to a value about 0.6 K lower than that in A1B, an offset which is approximately maintained throughout the simulated period. The continuing increase in greenhouse gases eventually causes ALL to warm to the original 2000 level, and then continue to warm up to 2060. However, the geoengineering delays any given amount of warming that would be produced under the A1B scenario by about 25 years. Figure 2 also shows the result of another short experiment where the geoengineering was turned off in 2025. The model warms rapidly (by approximately 0.4 K in the first 5 years), and within about 5–10 years the temperature is indistinguishable from that in A1B. This illustrates the fact that if geoengineering is used to “buy us some time”, then an even larger mitigation effort has to be undertaken when geoengineering is stopped to prevent extremely rapid global mean temperature increases, a point already made by Boucher et al. .
Figure 2. Evolution of near-surface air temperature anomaly (K) with respect to 1860 in HadGEM2-AO. The red line (A1B) indicates the simulation forced by the SRES A1B scenario, and the blue line (ALL) indicates the simulation that also includes the geoengineering of all three stratocumulus areas. The green line indicates a short simulation initialized from ALL at 2025 but with all geoengineering suspended. The envelopes around the lines are a measure of the interannual variability in the simulations, being ±1SD based on a detrended nine-point linear fit at each point.
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 Figure 3 shows the distribution of near-surface temperature change caused by the geoengineering, calculated as the difference between ALL and A1B averaged over the 30 years 2030–2059 inclusive. Each 30-year sample was detrended using a linear fit to the annual global means, and then a t test applied to the difference at each point. Only those points where the difference is statistically significant at the 5% level are plotted. The global mean cooling of 0.58 K is as expected from Figure 2; however, the distribution of the cooling is quite inhomogeneous. Over the ocean (mean temperature change −0.53 K) there are sizable areas of cooling in the tropics and subtropics associated with the areas of modified cloud, and also a fairly strong cooling response over the Arctic (up to −3 K). On the other hand, large areas of the Southern Ocean are not significantly affected. Over land (mean change −0.70 K), the response is again quite variable. Some areas, such as central Africa, Australia and India, experience over 1 K of cooling. On the other hand, areas such as Europe, the central U.S.A. and large parts of South America show no significant temperature change compared with A1B.
Figure 3. Mean 2030–2059 1.5m temperature change (K) due to geoengineering of the three main marine stratocumulus cloud areas (ALL − A1B). Areas where the difference is not statistically significant at the 5% level are in white.
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 Figure 4a shows the mean distribution of precipitation over land in A1B for the 2030–2059 period, and Figure 4b the change due to the geoengineering. The impact on precipitation over most land areas is insignificant, but there are obviously important exceptions. Sub-Saharan Africa and eastern Australia show increases of 0.2–0.6 mm day−1 (10–30%) compared with A1B, which could be beneficial in such low precipitation regions. Larger increases of up to 0.8 mm day−1 (>50%) are also present over northern India, another area of low precipitation (Figure 4a). Central Asia shows decreases of around 0.1–0.2 mm day−1 (∼20%), but perhaps the main area for concern is South America, where the Amazonia and Nordeste regions have decreases in precipitation over a large area, with reductions amounting to more than 50% in places.
Figure 4. Mean 2030–2059 land precipitation (mm day−1): (a) distribution in A1B; (b) ALL − A1B. Land areas in Figure 4b where the change is not statistically significant at the 5% level are in white.
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 The distribution of net primary productivity (NPP, a measure of the net carbon uptake by vegetation) and how this is affected by the geoengineering is shown in Figure 5. The distribution of changes in NPP largely corresponds to the changes in precipitation (Figure 4b), showing increases in sub-Saharan Africa, Australia and India, but it also shows a large impact in the north of South America, with reductions corresponding to 50–100% over a considerable area. These results suggest that, although this form of geoengineering might be successful in reducing global mean temperatures, and possibly having other beneficial effects for some regions, there are also potentially significant consequences for ecosystems in other regions such as the Amazonian rain forest.
Figure 5. Mean 2030–2059 vegetation net primary productivity (kg carbon m−2 a−1): (a) distribution in A1B; (b) ALL − A1B. Land areas in Figure 5b where the change is not statistically significant at the 5% level are in white.
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 To put these changes into context, it is worth knowing what the changes are owing to the A1B scenario alone. Figure 6 shows the mean change in near-surface temperature (Figure 6a), land precipitation rate (Figure 6b) and NPP (Figure 6c) between the 2030–2059 period in the A1B scenario and the 1970–1999 period in the historically forced simulation (see section 2). The temperature change shows the typical high-latitude amplification associated with ice albedo feedback, and strong warming of up to 4 K in many northern continental areas. In regions such as central and southern Africa, India and eastern Australia, the unmitigated climate change simulated under A1B causes a decrease in precipitation; the increases in these regions caused by geoengineering (Figure 4b) could therefore be important. The biggest signal is over the north of South America, where there is a large area where precipitation is reduced by over 1 mm day−1, an impact to which geoengineering adds up to a further 1 mm day−1 reduction (Figure 4b). The impact of the A1B scenario on NPP in this region is more mixed (Figure 6c), with some increases in the west (presumably due to CO2 fertilization) but reductions of up to 1 kg [C] m−2 a−1 in the east, which geoengineering further reduces by half as much again (Figure 5b).
Figure 6. (a) Change in annual mean 1.5 m temperature (K) between 2030–2059 in A1B and 1970–1999 in the historically forced HadGEM2-AO simulation. (b) As in Figure 6a, but for precipitation rate over land (mm day−1). (c) As in Figure 6b, but for NPP (kg [C] m−2 a−1). Areas where the changes are not statistically significant at the 5% level are in white.
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 While it is hypothetically feasible to deploy a fleet of cloud seeding vessels to all three of the stratocumulus areas shown in Figure 1, it is informative to investigate the climate response if such vessels were deployed to only one of these areas. The evolution of the global mean near-surface air temperature anomaly from 2000 to 2060 in NP, SP and SA compared with A1B is shown in Figure 7. This shows that the SP simulation is the coolest over most of the simulation period, whereas the SA simulation is largely indistinguishable from the A1B control. Figure 8 shows the distribution of changes in temperature and land precipitation for each case, as well as the sum of the individual responses (Figures 8g and 8h), averaged over the last 30 years of the simulations. These results confirm those from the HadGEM2-AML experiments (section 3.2 and Table 2) suggesting that modifying the South Pacific stratocumulus area has the greatest impact on global temperatures (Figure 8c).
Figure 8. Mean 2030–2059 changes in near-surface air temperature (K, left column) and land precipitation (mm day−1, right column) relative to the A1B control in HadGEM2-AO simulations (a, b) NP, (c, d) SP, and (e, f) SA. (g, h) The sum of the changes due to the individual stratocumulus areas (see Figures 3 and 4b). Changes which are not significant at the 5% level in Figures 8a to 8f are in white. For display purposes, Figures 8g and 8h have had the same significance masks applied to them as used in Figures 3 and 4b respectively.
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 Considering the impact on land areas of modifying the individual stratocumulus regions, Figure 8 again confirms that the SP case is the most effective in cooling the land, with North America being cooled by almost 1 K. On the other hand, it also shows that in the SA case many land areas are in fact warmed, the largest impact being a warming of up to 2 K over Amazonia. The biggest impact on land precipitation is also seen in the SA simulation (Figure 8f), with a reduction of over 1 mm day−1 over the Amazon region. This reduction due to a cooling in the South Atlantic mirrors a similar feature found in response to a warming of the North Atlantic due to decreasing levels of anthropogenic aerosols reported by Cox et al. . Amazonian rainfall has been shown to be intimately linked to the SST gradient across the Atlantic by shifting the patterns of moisture convergence and trade winds in many global models [e.g., Good et al., 2008; IPCC, 2007]. It appears likely that the decrease in precipitation over the north of South America seen in the ALL simulation (Figure 4b) and the consequent impact on the local ecosystem (Figure 5b) are due to the effect of the modifications to the South Atlantic stratocumulus area. Indeed, the sum of the individual effects shown in Figures 8g and 8h are very similar to the impacts of the ALL simulation (Figures 3 and 4b, respectively), indicating a high degree of additivity of the individual climate responses. Such linearity in the temperature and precipitation responses has been noted in coupled atmosphere-ocean simulations of increases in greenhouse gases and the direct effect of sulfate aerosol, albeit in models with much coarser resolution [Haywood et al., 1997]. This additivity becomes increasingly relevant when considering potential geoengineering solutions as it may be that several geoengineering solutions would need to be deployed to counteract the effects of global warming over centennial timescales.