Model trends during the RCP 8.5 climate change scenario

The UVic model has recently been evaluated in several model intercomparison exercises9,10,11 and generally has a similar response to CO 2 emission forcing as other models. Here we will briefly mention how the results of our RCP 8.5 scenario control run (Supplementary Figs 1–3) compared with other models. We will also point out the trends that differ from other models since these (possible) biases need to be taken into consideration when evaluating the model’s response to climate engineering. For surface air temperature the model simulates a global mean warming of 3.1 °C by the year 2100 relative to a 1986–2005 reference period (see also Fig. 2c for warming relative to the pre-industrial period), which is within the ranges of an EMIC intercomparison (1.6–4.1 °C)10 and the CMIP5 models (2.5–5.0 °C)12. Atlantic meridional overturning circulation decreases by 38% in our simulation, which is near the mean (40%) and within the range (36–44%) of 26 other models (mostly CMIP5 models)11. For ocean biogeochemistry a CMIP5 model intercomparison study has not yet been published so instead we have made comparisons to the Community Earth System Model (CESM1 (BGC)) RCP 8.5 scenario results13. By the year 2100 export production has decreased by 11% in our simulation and 13% in the CESM1 simulation. Net primary production decreases by 5.8% in our simulation and 5.7% in the CESM1 simulation. Mean ocean oxygen decreases by 7 μM in our simulation (see also Fig. 2g for the global total change in oxygen) and 10 μM in the CESM1 simulation. However, for the volume of the oxygen minimum zones (OMZ), our results are in contrast to CESM1, since our simulation shows a decrease in the size of the OMZs (Supplementary Fig. 4) and theirs show an increase of 24%. Since most Earth system models have problems simulating oxygen distributions and the volume of OMZs it is difficult to determine which trend is correct13,14,15, although observations suggest that OMZs have been expanding in recent decades16. Evaluating terrestrial carbon uptake or loss is difficult since there are large differences (that is, uncertainty) in total carbon uptake between the CMIP5 models (ranging from −0.97 to 2.27 Pg C y−1 during 2006–2100), with some models predicting that the terrestrial biosphere will become a net source of carbon and other predicting that it will become a larger sink17. In our simulation the terrestrial biosphere becomes a larger sink with a terrestrial carbon pool in the year 2100 (Table 2) that is 15 Pg C larger than the highest value reported for the CMIP5 models17. For soil moisture our model indicates that globally soil moisture will decrease in June, July and August and increase in December, January and February (DJF). The first result is largely consistent with CMIP5 model results18. However, the majority of CMIP5 models indicate that while a large proportion of the globe will have higher soil moisture in DJF an even larger proportion will have drier conditions18, which is inconsistent with our results. For precipitation our model does not show a global increase of 1–3% K−1 as warming occurs, which is inconsistent with most models19,20,21. Instead global precipitation in the model decreases by 0.3% K−1 (see also Fig. 2e and Table 3 for mean annual changes). This inconsistency occurs despite an increase in atmospheric water vapour of 7.4% K−1, which is consistent with other models, observations, and in line with the Clausius–Claperyron equation19,20,21. The decrease in global precipitation is entirely due to a decrease in terrestrial precipitation of 3.41% K−1 since precipitation and evaporation over the ocean increase by ~1.26% K−1, which is in good agreement with other models (Supplementary Fig. 5). The decrease in terrestrial precipitation is related to the simulated sensitivity of evapotranspiration to increasing CO 2 (that is, the physiological response of plant stomata to CO 2 fertilization and its effect on water vapour loss), which results in a reduction in terrestrial evaporation of 8% K−1. While this strong decrease in evapotranspiration is inconsistent with the general trend of other models (despite significant differences and uncertainty among them22), recent research23 has suggested that existing theory and the 13 most commonly used terrestrial biosphere models underestimate forest water-use efficiency when CO 2 increases.

Figure 2: Comparison of climate engineering method effects on key global properties. Simulated changes in globally averaged annual atmospheric CO 2 and surface air temperature (relative to a pre-industrial temperature of 13.05 °C) and the total amount of annual global precipitation and ocean oxygen for model runs where climate engineering was continuously deployed (a,c,e and g) and runs where it was discontinued after 50 years (b,d,f and h). The control run, with no climate engineering, is also shown. Full size image

Table 2 Climate engineering induced changes in global temperature and carbon inventories. Full size table

Table 3 Climate engineering induced changes in key Earth system properties. Full size table

Climate engineering method effects on temperature and CO 2

When climate engineering is deployed continuously from the year 2020 onward, atmospheric CO 2 is reduced in all of the simulations (Fig. 2a, Table 2). However, these reductions are small compared with expected business-as-usual anthropogenic emissions, and atmospheric CO 2 continues to increase rapidly and still reaches more than twice the current level by the end of the century in all simulations. Those methods that have been proposed to reduce atmospheric CO 2 as a means of preventing warming, that is, carbon dioxide reduction (CDR) methods (Table 1), are thus, as expected from other studies3,5,24,25,26,27, unable to prevent a 2.7–3.9 °C mean temperature increase (temperature increases by 3.8 °C with no climate engineering) in our model simulations under the RCP 8.5 emission scenario by the year 2100 (Table 2, Figs 2c and 3a,c,e,g). Indeed, the albedo change caused by simulated AF (Table 3, Supplementary Fig. 6b) actually results in more warming (see text below for details) than if no climate engineering was implemented. SRM is the only method in our simulations that is potentially able to restore the temperature to a near pre-industrial value within the twenty first century (Table 2, Figs 2c and 3i) and to prevent many warming-related Earth system changes from occurring (Supplementary Figs 6i,j, 7i,j, 8j and 9e). However, as already known from other studies employing different scenarios28,29, a persistent reduction of warming requires that SRM is applied continuously.

Figure 3: Climate engineering method effects on temperature and carbon storage. The simulated year 2100 mean annual differences between the climate engineering runs and the control run (climate engineering run minus the control run) for surface air temperature (a,c,e,g and i) and terrestrial and oceanic carbon inventories (b,d,f,h and j). Note the difference in the surface air temperature scale for solar radiation management. Full size image

Discontinuation of climate engineering

If SRM is discontinued at any time then extremely rapid warming occurs (up to 0.36 °C y−1), along with an increased rate of atmospheric CO 2 accumulation (Fig. 2b,d; Supplementary Fig. 10). For temperature, this termination effect is well known and has been consistently reproduced in SRM studies28,29,30,31,32. However, there is much less certainty concerning the response of the carbon cycle to SRM discontinuation30. Here we show that there is a substantial termination effect on the carbon cycle since the reduction in atmospheric CO 2 , which for the simulated SRM deployment is even larger than that of the CDR methods (Table 2, Fig. 2a), quickly reverses with atmospheric CO 2 levels reaching those of the control run within a few decades (Fig. 2b). For OU, rapid warming (up to 0.33 °C y−1) also occurs when it is discontinued, with surface air temperature ultimately becoming 0.24 °C higher, due to the method’s impacts on the planetary energy budget (see below and ref. 27, than if the method had not been deployed at all. Atmospheric CO 2 concentrations also rise again with the discontinuation of OU until they are at essentially the same level as if no climate engineering had occurred. If the other methods are discontinued less dramatic changes occur. When OIF and OA are discontinued then the ocean stops taking up CO 2 at a higher rate (Fig. 2b; Supplementary Fig. 10). However, essentially all of the CO 2 that was taken up (Fig. 3d,f) remains in the ocean throughout the duration of the simulation and atmospheric CO 2 concentrations are 21 (OIF) and 48 (OA) p.p.m. lower in the year 2100 than if the methods had not been deployed at all. Because of this, surface air temperatures in the OIF and OA simulations always remain slightly lower, by 0.09 and 0.18 °C by the year 2100, respectively, than in the control run (Fig. 2d; Supplementary Fig. 10). For OIF, this result is consistent with an earlier study26.

When AF is discontinued by stopping irrigation, the afforested desert regions eventually return to a desert state and the carbon that was stored in the plant biomass and soil (Fig. 3b) is slowly returned to the atmosphere through decay and respiration. As the vegetation related albedo change ends the surface air temperature drops from slight slightly higher (0.1 °C) to slightly lower (0.08 °C) than the control run (Table 3, Supplementary Fig. 6; see also text below), which is an effect that has been seen in other studies of deforestation33. The temperature becomes slightly lower than in the control run because atmospheric CO 2 is lower since not all of the stored carbon has returned to the atmosphere by the year 2100 and because during desertification the deserts expand (~1–3 model grid cells; not shown) beyond their former range (for example, the desert areas in the control simulation). Since more desert area increases the land surface albedo this contributes to the slight decrease in surface air temperature. The expansion of the deserts occurs because during simulated AF the vegetation in the grid cells next to the irrigated regions receive more water due to runoff and additional precipitation (see text below) and thus the vegetation changes from grass land (C3 and C4 types), as in the control simulation, to a shrub type. When simulated irrigation stops, the vegetation is unable to revert from a shrub type to grass types in our model world that has undergone climate change and instead becomes a desert. Similar shifts in vegetation have been observed in other simulations in response to perturbations34.

Efficacy of the climate engineering methods

Although most of the CDR methods (Table 1) appear to gradually affect atmospheric CO 2 and the surface air temperature (Fig. 2a,b), their CDR efficacy is actually quite non-linear with time (Fig. 4b–d), and there is often a backflux of carbon from non-targeted areas as they adjust to the change in atmospheric CO 2 (for example, the ocean in the case of AF; backfluxes may also occur within the reservoirs). Indeed, even with SRM, whose purpose is to gradually reduce incoming solar radiation (Fig. 4a) to regulate the surface air temperature, the effectiveness-amplifying (in terms of surface air temperature reductions) CDR side effects (mainly on terrestrial productivity and soil respiration; see text below) are substantial (Fig. 4b–d; Supplementary Fig. 11). Some CDR methods like AF, OIF and OU are quite effective on short time scales (peak C removal occurs in the first 5 years), but less so on longer ones, a result that has been previously shown for OIF and OU25,26,27. In contrast, CDR for OA increases in effectiveness over time (Fig. 4b–d) because the ocean-buffering capacity35 remains high, unlike in the other simulations, even as atmospheric CO 2 increases, thus allowing the ocean to take up more CO 2 . For SRM, CDR per change in insolation is highest during the first few years, then declines, and then gradually increases in intensity again (Fig. 4b–d, Supplementary Fig. 11). The change in surface air temperature per change in insolation for SRM occurs mostly during the first decade of deployment (Fig. 2c, Supplementary Fig. 11). These results highlight the non-linear relationship between changes in temperature and CO 2 , illustrated in Fig. 4d, that occur because of other, often method-specific, feedbacks involving, among others, the hydrological cycle, vegetation and ocean chemistry (see text below).

Figure 4: Comparison of climate engineering method effectiveness. (a) Insolation at the top of the atmosphere for the SRM (yellow line) model run and all other simulations (dotted red line). The climate engineering model run differences (relative to the no climate engineering model run) in the annually averaged fluxes of carbon from the atmosphere to the (b) land and (c) ocean. Comparison (d) of surface air temperature versus atmospheric CO 2 differences (relative to the no climate engineering model run) for the climate engineering simulations. Full size image

Side effects of the climate engineering methods

All of the methods have unintended side effects. Here we mainly focus on physical and biogeochemical side effects that can be diagnosed with the current model (Tables 3 and 4), but we will also mention some other commonly discussed ones. When possible, we have tried to indicate whether a system or cycle will be perturbed by a side effect, but in most cases we are unable to make robust inferences on specific responses to a perturbation (for example, how species or small-scale regions may be affected).

Table 4 Side effects of the climate engineering methods. Full size table

The side effects of AF tend to be regional, with the large-scale regions defined here as N. Africa, Australia and the surrounding few hundred kilometres. However, although regional changes would certainly be expected, caution must be taken when interpreting the robustness of these results since the model has fixed atmospheric circulation patterns that essentially do not change (see methods) in response to AF. Regional precipitation and terrestrial productivity both increase (Table 2, Supplementary Figs 6a and 12a,c), which could be beneficial for some species and, presumably, humans as long as the increase in precipitation does not cause severe storms or flooding. However, these changes would have a large and perturbing impact on local ecosystems and any societies that depend on them. Productivity increases because in addition to the direct effects of having more water from irrigation, which allows plants to grow in a former desert, there is the fertilizing effect of increasing CO 2 on growth23. Increasing CO 2 during the simulations has a further effect on vegetation and the hydrological cycle because vegetation water-use efficiency increases with increasing atmospheric CO 2 concentrations22,23,36. Although the increase in precipitation is regional it increases the total amount of global precipitation (Fig. 2e) because more water is added to the hydrological cycle. The higher soil moisture and precipitation, which also result in more terrestrial runoff into the ocean, thereby decrease coastal salinity (Supplementary Fig. 7b) and raise the global mean sea level by ~13 cm in the year 2100, unless the water used for irrigation is desalinated seawater. As a result of this change in salinity regional ocean circulation patterns (Supplementary Fig. 8b) and ecosystems would likely be affected as well. Increasing the amount of soil moisture also results in evaporative cooling (Supplementary Fig. 8a). However, this cooling is countered by a decrease in the regional surface albedo (Supplementary Fig. 6b) when the more reflective desert is replaced by darker vegetation. As a result the air temperature in the region increases (Fig. 3a) because of a sensible heat gain. Present-day AF simulations in boreal areas have produced similar albedo-related increases in temperature37. Another interesting, but not simulated, potential side effect of AF is that there would likely be less dust blown off of these desert regions if afforested (that is, both wind patterns and sources of dust may change). Since this dust contains iron and other nutrients that are known to fertilize the ocean38 and distant forests such as the Amazon, it is possible that productivity in these regions could decrease39,40. Thus, an increase in productivity and carbon uptake in the afforested areas could result in a decrease in other areas, making this method potentially less effective as a carbon sink40.

Both OU and OIF increase local marine productivity (Table 3, Supplementary Fig. 6e,g), which may be beneficial for some species and fisheries27. However, the ecosystems of these fertilized regions (throughout the water column) would be disrupted by these changes26,27. Indeed, because of all the carbon that is locally sequestered and then respired during OIF, pCO 2 increases and the pH decreases more rapidly near Antarctica than in the control run (Supplementary Fig. 13e,f). Other OIF studies26 have reported a similar result with regional declines in pH of up to 0.15 units (relative to a control run). A similar effect (pCO 2 increase, pH decrease) occurs during OU when sequestered carbon is upwelled, for example, in the tropical upwelling regions (Supplementary Fig. 13g,h). These methods also decrease productivity in some unfertilized regions because more nutrients are utilized before being transported out of the fertilized region25,26. Thus, non-local ecosystems and fisheries could also be affected.

As has previously been shown for OIF26, it and OU increase ocean deoxygenation (Fig. 2g; Supplementary Fig. 9c,d), due to the respiration (data not shown) of fertilization-induced organic matter in deeper waters. As a result the size of suboxic (O 2 <10 μM) regions changes (Supplementary Fig. 4), impacting marine ecosystems and biogeochemical cycles26,27. The simulated 265% increase in the size of the suboxic zones caused by OU may be particularly important because it could have climatic feedbacks due to the production of methane and N 2 O, which are potent greenhouse gases, in low-oxygen waters27,41. Although the overall percent fraction that is suboxic with OIF decreases by 43% because of less productivity, export and respiration outside of the fertilized area26, oxygen declines in the Southern Ocean in our model (Supplementary Fig. 9c), with some regions becoming suboxic by the year 2125. This OIF-induced oxygen decline is much stronger than in an earlier OIF modelling study26 that found only minor decreases in oxygen (60 mmol m−3) in the Southern Ocean by the year 2110. Since most of the oxygen is consumed in deeper waters that will not be at the surface, where air–sea gas exchange can occur, for tens to hundreds or thousands of years, discontinuing OIF and OU does not restore oxygen to the state that it is in the control simulation (Fig. 2h).

For OU there are also a number of simulated side effects related to the cooling of the Earth’s surface. Terrestrial productivity is perturbed and in most places there is less soil respiration (Table 3, Supplementary Figs 6g and 9h), which together results in more terrestrial C storage (Fig. 3h, Table 2). However, unlike in an earlier OU study27 where 80% of sequestered C was stored in the terrestrial system by the year 2100, only 54% was in our study, which can be explained by the consideration of iron limitation and iron upwelling in our study (see Methods). Precipitation patterns are also altered (Supplementary Fig. 12g,h), with a decrease in total precipitation (Fig. 3e, Table 3) because of decreases in evaporation and evapotranspiration28. In addition, as in an earlier study27, OU decreases sea-ice loss and perturbs ocean temperature, salinity, stratification and circulation (Supplementary Figs 7g,h, 8g,h and 14b). Together, these side effects alter the planet’s heat-budget and cause the Earth to take up additional heat and store it in the ocean’s low-latitude subsurface waters (Supplementary Fig. 14b; see ref. 27 for further details). As a result this method cannot be stopped without causing rapid warming that ultimately exceeds that of the control run27 (Fig. 2d).

OA has few side effects that can be diagnosed with our model. The ones that we can diagnose reduce the rate of ocean acidification (Supplementary Fig. 13d) and keep the saturation states of aragonite and calcite higher than with most other methods (Table 3), despite higher oceanic carbon uptake (Fig. 3d, Table 2). However, other studies have noted that there are likely to be other side effects that impact marine ecosystems24,42 and shown that certain species physiology would be altered by OA43. Furthermore, this method is also likely to have a substantial impact on terrestrial systems due to the mining and production process associated with the alkalizing agent24,42.

For SRM there are a number of side effects related to the cooling of the Earth’s surface. Terrestrial and marine productivity are perturbed and there is less soil respiration (Table 3, Supplementary Figs 6i and 9j), which ultimately results in more terrestrial C storage (Table 2). For global terrestrial NPP, which is 1.7 Pg C y−1 higher in the year 2100 than in the control run, this increase is consistent with other SRM studies (for example, in the GeoMIP G1 experiments the model’s mean change in NPP between the SRM runs and the quadrupled CO 2 control runs was 1.8 Pg C y−1)44,45. Interestingly, this accumulation of C on land decreases surface-ocean pCO 2 and reduces the rate of ocean acidification (Table 3, Supplementary Fig. 13i,j). Precipitation patterns are also altered, with a decrease in total precipitation (Fig. 3e; Table 3, Supplementary Fig. 12i,j), because of less evapotranspiration28 and evaporation22,46. Other studies22,28,44,47 have found a similar decrease in precipitation when SRM is implemented. However, since evapotranspiration in our model is more sensitive to CO 2 increases than in other models and precipitation is simulated with a simple energy–moisture balance model, we performed the GeoMIP G1 experiment48 to gain a better understanding of how this particular result compares with other SRM experiments. This experiment (G1) involves instantaneously quadrupling the CO 2 concentration (from pre-industrial levels) while simultaneously reducing the solar constant to counteract this forcing (50-year simulation). In this experiment we found that global precipitation decreased by 0.26 mm d−1 (a 9% reduction), which is more than the mean 0.1 (range: 0.1–0.2) mm d−1 decrease (a mean reduction of 4.5%) found with the GeoMIP models44. While this appears to be a substantial difference, when we spatially compare the results of last 10 years of our G1 simulation with the results from one of the GeoMIP models (MPI-ESM-LR), which has itself been comparatively assessed44, we are able to discern that the regional changes are higher in the MPI-ESM-LR even though the global mean effect is smaller (Supplementary Fig. 15). Thus, these GeoMIP model results actually indicate that larger, more extreme SRM-induced changes in regional tropical precipitation are possible, even if the global mean change is less than in our simulations. These differences likely reflect the differences in the atmospheric model components (for example, UVic’s simple atmosphere versus the more complex, higher resolution one in the MPI-ESM-LR).