We conducted three different aerosol-based geoengineering experiments using the fully coupled NorESM1-ME, with which we investigated the impacts of idealized scenarios of aerosol-based geoengineering under the high-CO2 RCP8.5 and the target temperature scenario RCP4.5. NorESM1-ME is based on the Community Earth System Model CESM;. Some of the key differences in NorESM1-ME from CESM are (1) a more sophisticated tropospheric chemistry–aerosol–cloud scheme , (2) a different ocean circulation model based on the Miami Isopycnic Coordinate Ocean Model (MICOM) with extensive modifications , and (3) the ocean biogeochemical model, which originated from the Hamburg Oceanic Carbon Cycle (HAMOCC) model . Both the land and atmospheric components have a horizontal resolution of 1.9∘ latitude × 2.5∘ longitude with 26 vertical levels in the atmosphere, whereas the ocean model employs a displaced pole grid with a nominal ∼1∘ resolution and 53 isopycnal layers.

The land component of NorESM1-ME is CLM4 . The land C cycle module in CLM4 includes carbon–nitrogen (CN) coupling that is prognostic in CN and vegetation phenology. As a result, plant photosynthesis is also limited by the nitrogen (N) availability . The CLM4 has separate state variables for C and N, which are followed through separately in leaf, live stem, dead stem, live coarse root, dead coarse root, and fine root pools. There are two corresponding storage pools representing short-term and long-term storage of non-structural carbohydrates and labile N. Sources and sinks of mineral N are implemented in the form of atmospheric deposition, biological N fixation, denitrification, leaching, and losses due to fire events. The CLM4 photosynthesis uses both direct and diffuse radiation for sunlit leaves and only diffuse radiation for shaded leaves . The plant functional types (PFTs) and land cover change distribution in CLM4 are prescribed and updated annually according to the Coupled Model Intercomparison Project phase 5 (CMIP5) global land use and land cover change dataset . The transient PFT and land cover fields take into account historical and future climate change under the RCP8.5 scenario (1850–2100); these were implemented using the harmonized land use change scenarios and Integrated Assessment Model, respectively. Details on PFT, terrestrial C and N cycling, and land cover implementation in the CLM4 model are described in . For this study, NorESM is run with a fully interactive prognostic C cycle (i.e., in emission-driven mode).

Two of the RM methods used in this study aim to reduce the amount of solar radiation reaching the surface to alleviate global warming through spraying of aerosols into the atmosphere: SAI e.g., and MSB . Another technique aims to increase the amount of outgoing thermal radiation to space by reducing the cover of high-level ice clouds: CCT . Increasing application of RM was used to lower the total radiative forcing in the RCP8.5 baseline simulation down to a temperature level corresponding to RCP4.5, as described in and similar to the G6sulfur experiment of GeoMIP . The RM is started in the year 2020 on the background of the RCP8.5 scenario and continued until the end of the century. The mean of three ensemble members was used for each case. In the year 2101 the RM was ended. One ensemble member was extended for another 50 years for each case such that the effects of sudden termination of large-scale RM may be assessed.

The aerosol-based RM experiments were implemented as follows.

The three RM methods alter the direct visible radiation (DVR) and diffuse visible radiation (FVR) in different directions, with little impact on the level of atmospheric CO2 concentrations (Fig. 1). The differences in direct and diffuse radiation are attributed to how the radiation management methods are implemented; each differs in affecting longwave and shortwave radiation . Regardless of the methodological differences, all RM methods are able to reduce the net radiation at the top of the atmosphere and the global mean air temperatures close to the RCP4.5 level as expected. Global land surface air temperature (TSA) increases at a slower rate until the end of the 21st century under all three RM scenarios compared to the baseline RCP8.5 scenario, in which there is approximately 2.3 ∘C of difference between the RM and non-RM world at the year 2100. Under CCT application, the increase in global precipitation is somewhat higher than RCP8.5 as explained in . CCT keeps the level of precipitation close to RCP8.5 until the year 2100 due to an amplified hydrological cycle from increased latent heat flux . CCT has been shown to lead to an increase in precipitation in previous studies , whereby the radiative cooling of the troposphere increases the latent heat flux at the surface and hence alters the precipitation rates. SAI shows a reduced rate of increase in global precipitation similar to RCP4.5. Under MSB application, the rate of global precipitation increase falls between the SAI and CCT.

There is a large overall increase in global mean NPP until the end of the 21st century in the RCP8.5 scenario and under the three RM scenarios (Fig. 1), whereas only a small increase in NPP is simulated under the RCP4.5 scenario. At the same time, there is a large increase in the rate of soil organic matter decomposition (heterotrophic respiration: HR) in the RCP8.5-based experiments. Relatively small NPP differences are observed between RCP8.5 and the RM simulations compared to the RCP4.5 scenario. This illustrates that the CO2 fertilization effect is much larger in regulating NPP than the effects of temperature and precipitation, as the levels of temperature and (in the case of SAI and MSB) precipitation are similar between the RCP4.5 scenario and the three RM methods on a global scale.

There are no discernable spatial patterns shown in the changes in direct and diffuse radiation except that changes in direct radiation under the CCT application are more concentrated in the tropics. The SAI method shows considerably increased diffuse radiation throughout the global land areas compared to the baseline RCP8.5 scenario (Fig. S2). While TSA exhibits similar patterns across different RM applications, the precipitation patterns are more variable over space across different RM methods. The global spatial patterns of precipitation towards the end of the century (mean of 2070–2100; Fig. 2) show that CCT generally increases precipitation in the tropics and Mediterranean region relative to RCP8.5. In particular, MSB tends to increase precipitation over extratropical land more than SAI due to the regional application of the forcing . The spatial patterns of precipitation change in MSB mostly follow those of CCT, but the magnitude of change is smaller. On the other hand, SAI shows overall decreases in precipitation, particularly in the tropics, relative to RCP8.5. All three methods show a decrease in precipitation in the East Asia region.

The differences in temperature and precipitation across the tree RM methods in different land biomes of the world show that there is no noticeable difference in mean annual temperature across the three different RM methods (Fig. 3). There is a cooling imbalance across the three RM forcings; the tropics tend to cool more than high latitudes and cooling is more pronounced in the ocean than on land, with a stronger southern hemispheric cooling for CCT . We show that precipitation patterns vary across the three methods in different biomes. In all biomes, SAI application results in the largest decrease in precipitation, followed by MSB, relative to the RCP8.5 scenario. Under CCT application, precipitation even increases beyond the RCP8.5 level. The precipitation differences across the three methods are large, particularly in the tropics and the midlatitudes, where CCT application results in higher precipitation rates than the other two methods. The differences in precipitation are amplified over time until the end of the 21st century. According to , shortwave-radiation-based geoengineering methods exhibit a strong reduction in global precipitation levels relative to RCP8.5 but also relative to RCP4.5. CCT leads to a slight increase in global precipitation, even over the RCP8.5 levels; however, land precipitation patterns in different biomes vary. Aggregated over all biomes, precipitation changes are much smaller than over the total (ocean + land) area. Particularly, precipitation is not reduced much below the RCP4.5 levels for SAI and MSB, as in (compare their Fig. 2 with Fig. 1 in this study).

The spatial patterns and the magnitude of NPP change under the three methods show distinct differences. There are common spatial patterns in the NPP decrease in the northwestern part of Amazonia, equatorial Africa, and eastern Asia in the three RM experiments (Fig. 2). But overall, the large increase in NPP in Europe and equatorial South America, particularly for the CCT experiment, compensates for the decreases elsewhere, hence creating a general lack of deviation as a whole from the RCP8.5 scenario (see Fig. 1). It is clear from the comparison shown for precipitation (Fig. 2) that the NPP changes are most correlated with the spatial changes in precipitation.

Under the CCT application, there is a strong increase in NPP in the tropics and the Mediterranean region but a decrease in East Asia. MSB does not show a noticeable change except increased NPP in eastern Amazonia. The spatial pattern of NPP in MSB is similar to CCT, but the magnitude is smaller in MSB. There is a strong decrease in NPP under SAI application, particularly in the tropics. These overall patterns follow similar spatial patterns as the precipitation and are highly correlated as expected (Fig. S3). The differences in NPP are largely dominated by three biomes: tropical forest, grass–shrubland, and temperate forest (Fig. 4). NPP and HR in MSB and SAI simulations negatively deviate from the RCP8.5 simulation, whereas in CCT both remain at a similar level as RCP8.5 in tropical forest, grass–shrubland, and temperate forest, likely due to an increased precipitation level in these biomes. But since temperature is a stronger regulator of NPP and HR in high-latitude biomes, CCT simulations also exhibit decreased NPP and HR compared to the RCP8.5 scenario. Additionally, we do not observe any noticeable changes in seasonality for NPP and leaf area index (LAI) between RM methods and the RCP8.5 scenario (Figs. S4 and S5) as seen in . Although there is spatial variability in precipitation patterns, there is no change in seasonality between the three RM methods and the baseline RCP8.5 scenario.

Overall, the varying precipitation patterns may be the strongest driver of the responses of global-scale C uptake and release. Changes in diffuse radiation are found to affect photosynthesis as diffuse radiation can be more efficient in photosynthesis e.g.,. Under these assumptions, increases in diffuse radiation and decreases in direct radiation under SAI are expected to increase plant photosynthesis . Increases in diffuse radiation are known to positively affect photosynthesis up to a threshold of the ratio between diffuse and total radiation at around 0.4–0.45 . Across the three RM methods, this ratio ranges from 0.29 (CCT) to 0.4 (SAI) at the end of the RM application in the year 2100. The responses of NPP under changes in diffuse radiation to different RM applications exhibited in our study suggest that changes in diffuse radiation may not be as large a driver of NPP change at the global level as temperature and precipitation. Under the coupled framework of an ESM, it is very difficult to decompose the direct single effects of climatic factors due to interactions , and separate simulations are necessary to directly quantify this. The N limitation implemented in the CLM4 has been shown to limit C uptake by 74 % relative to the C-only model , but CLM4 still exhibits NPP biases in the tropics . It is important to note that despite the strong CO2 fertilization and the increase in diffuse radiation, NPP in some parts of the tropics decreases under the SAI application, likely due to the strong decrease in precipitation (Figs. 2, S3 and S4).

Vegetation C storage in different biomes illustrates that global vegetation C storage changes are dominated by the responses of the tropical forest biome (Fig. 5). Under the baseline RCP8.5 scenario, global vegetation C storage decreases due to reduced tropical forest, temperate forest, and grass–shrubland area as part of the land use change scenario used in RCP8.5 . Compared to the baseline RCP8.5 scenario, vegetation C in Arctic tundra, boreal forest, and tropical forest biomes are affected the most under RM applications. In Arctic tundra and boreal forest biomes, all three RM scenarios result in a slightly reduced accumulation of vegetation C compared to the RCP8.5 scenario, likely due to decreased temperature, exhibiting the temperature limitation in high-latitude biomes. In tropical forest, SAI application reduces vegetation C storage relative to the RCP8.5 scenario, but CCT application slightly increases C storage due to increased precipitation (Fig. 2). The magnitude of change in global vegetation C at the end of the century due to application of different RM methods is up to 10 PgC. On the other hand, the magnitude of vegetation C reduction due to the different underlying land use change scenarios in RCP4.5 and RCP8.5 is up to 100 PgC (Figs. 5, S7). These differences are attributed to increased forest and grassland area as part of the RCP4.5 scenario . This highlights the fact that large-scale changes in vegetation C storage depend much more on anthropogenic land use change than on additional perturbations caused by application of RM in our simulations.

In tropical forest, the differences in vegetation C storage appeared to be correlated with precipitation patterns, whereby decreases and increases in precipitation in the three different methods regulate vegetation C storage. Differences in vegetation and soil C storage in the temperate zone (temperate forest and grass–shrubland), however, did not always correspond directly to varying precipitation patterns. For instance, an approximately 100–120 mm difference in mean annual precipitation shown in temperate forest and grass–shrubland biomes between the SAI and CCT methods does not portray differences in vegetation C storage (Figs. 3, S6).

Upon sudden termination of RM applications, the levels of radiation, temperature, and precipitation quickly converge to the baseline RCP8.5 scenario (Figs. 1, 3, 4). Note that the temperature does not increase to exactly the same level as the RCP8.5 scenario, which has been observed in previous studies and is due to the thermal inertia of ocean heat uptake . As the temperature and precipitation patterns converge towards the RCP8.5 scenario, NPP also becomes similar to the RCP8.5 scenario (Fig. 1). The soil C storage decreases as RM is terminated, and towards the end of the simulation in the year 2150, soil C storage in all three RM methods is at a similar level (Fig. S6), but the magnitude is still higher than under the RCP8.5 scenario by 10 PgC globally. The likely accumulation of soil C under RM applications may be viewed as one of the positive effects of geoengineering, which was supported by a recent multimodel comparison study . Globally, land C accumulation associated with RM would remain on land for at least 50 years following termination . Although the termination effects seem catastrophic due to their rapidity in particular, some studies suggest that realistically the most extreme cases would be unlikely as termination could be avoided by geopolitical agreement once deployed .

Reduced atmospheric temperature and precipitation under RM have large effects on vegetation C storage compared to the baseline scenario, RCP8.5. Under the RCP4.5 scenario, the rate of C uptake denoted as NPP is slower due to reduced temperature, precipitation, and atmospheric CO2 levels (Fig. 1). However, global vegetation C storage is far greater than RCP8.5 and the three RM simulations, which are based on underlying RCP8.5 scenario assumptions (Fig. 5), due to the larger forest and grassland areas in the RCP4.5 scenario . As a result, the difference in global vegetation C between the RCP4.5 scenario and the rest of the RCP8.5-based scenarios is nearly 170 PgC. This strongly suggests that on a global scale, areal changes in vegetation and land surface management play very important roles when accounting for global-scale vegetation C storage. We suggest taking this point into account when comparing the different pros and cons of technological applications such as geoengineering and mitigation options such as afforestation.

Our results suggest that even with reduced temperature stress created by RM applications, the productivity of vegetation in the three most productive biomes on Earth may be reduced due to changing precipitation patterns (particularly SAI). Therefore, considering the changes (i.e., reduction) in precipitation alone, RM may have negative effects on non-irrigated crops or food production globally. Nevertheless, the effects of CO2 fertilization in the future are suggested to compensate for the deleterious impacts of both RM-induced temperature and precipitation changes . Although not directly investigated in this study, different RM methods have been shown to induce various climate extremes in addition to mitigating them, which will have profound effects on the physiology of vegetation . Indeed, some studies show seasonal variation in temperature under geoengineering , although we did not observe this in our study. This is not within the scope of our study, but it could be an interesting point to consider in future studies.

We acknowledge that CLM4 has numerous limitations that prevent it from accurately estimating global-scale soil C storage, and therefore we do not make an estimation of global soil C storage. But here, we compare soil C storage under different methods to understand the factors controlling the difference across the three RM methods. Soil C storage increases under RM applications compared to the baseline RCP8.5 scenario (Figs. S6 and S7) because the decrease in temperature slows the rate of soil organic matter decomposition by microorganisms. An increase in total soil C is also simulated under the RCP4.5 scenario (Fig. S7) likely due to the combination between increased vegetation C accumulation and slight reduction in soil respiration. There is an increase in soil C storage under the RCP8.5 scenario in the early 21st century due to increased NPP, but ultimately soil C decreases quickly due to accelerated soil respiration (Fig. 1). In different biomes, temperate forest exhibits the largest difference across the three RM methods; soil C storage under the SAI method is nearly 1.0 PgC higher than CCT at the end of the 21st century. This is likely due to lower precipitation in SAI, which reduced the rate of decomposition.