Our study focuses on results from Earth System Model simulations from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) for three scenarios, namely SSP245, SSP585 and G6sulfur . Broadly, SSP585 is a fossil fuel intensive high emissions scenario, while SSP245 is a medium radiative forcing scenario with medium challenges to conventional mitigation, which assumes that historical trends continue without substantial deviations . Both scenarios run from 2015 to 2100. G6sulfur is part of the Geoengineering Model Intercomparison Project (GeoMIP). G6sulfur sets out to reduce the forcing from the high forcing scenario (SSP585) to the medium forcing scenario (SSP245) using stratospheric sulphate aerosols injected in a line from 10° S to 10° N along a single longitude band (0°), starting in 2020 and continuing to 2100. The amount of sulphate injection required to reduce radiative forcing to that of the medium forcing scenario SSP245 varies between models and the necessary amount is calculated on an individual model basis . Table 1 highlights differences between how individual models simulate G6sulfur. Fires are an important mediator of forest dieback and form part of a positive feedback loop that can lead to abrupt losses of vegetation. Thus, when analysing the simulation of dieback dynamics, it is important to identify which models explicitly represent fire processes (Table 2). All three scenarios were concatenated onto historical runs (1850–2014) for each model. G6sulfur simulations begin in 2020, and so for the missing five years from 2015–2019 SSP585 data is used to bridge the gap. MPI and CNRM initiate the G6sulfur simulations slightly earlier than the other modelling groups (2015 rather than 2020). As the baseline experiment for G6sulfur is SSP585, the years preceding the onset of SAI in 2020 correspond to SSP585 simulations. Therefore, after concatenation, all models can be treated the same.

For this study, single ensemble members from five climate models (provided in Table 2) that performed the G6sulfur CMIP6 simulation are used . We examine the model output diagnostics for terrestrial carbon storage (cLand) and net primary productivity (NPP) to assess the impact of the SAI (G6sulfur) GeoMIP experiment on the terrestrial carbon cycle. Four of the models used in this study show good agreement with observations for land ecosystems and the carbon cycle according to the IPCC AR6 report (the fifth, CNRM-ESM1-2, was not assessed in this chapter of the AR6; ). We also analyse surface temperature and precipitation differences as these are key for explaining the differences across the scenarios. All data from the CMIP6 experiments were first linearly interpolated onto a world grid with 1° × 1° resolution. Analysis of the Amazon used a region defined by the latitudes 20° S to 14° N and longitudes 83 to 34° W.

Although it is impossible to evaluate SAI runs against observations, we can use volcanic eruptions as a proxy. We see consistent spatial patterns between models in response to the volcanic eruptions of Pinatubo, El Chichon, Agung, and Krakatoa (Fig. ), which are comparable to those observed in atmospheric inversion models . The magnitude of the land carbon flux anomalies in the models we analyse are comparable to those from , with both experiencing land carbon anomalies that lie predominantly in the range ±70 gCm-2yr-1. The land carbon anomalies in high latitudes are relatively small in both examples, with more notable changes occurring around the equator and low latitudes. Of all the models, MPI-ESM1-2-LR experiences the greatest area of notable decreases in the land carbon sink, though similar decreases are also observable in CNRM-ESM2-1, IPSL-CM6A-LR and UKESM1-0-LL. Meanwhile, UKESM1-0-LL appears to have the largest areas of notable increase in the land carbon sink due to volcanic eruptions, with these increases especially prominent in southern regions of the African continent. Observed responses to individual volcanic eruptions differ due to differences in the ENSO phase , which are typically different to the ENSO phase being experienced in the models for the same year. In fact, one study finds that the response of atmospheric CO₂ to volcanic eruptions can be as much as 60 % larger during El Niño and winter , highlighting the difficulty of directly comparing the response to volcanic eruptions between models and observations. Despite this, we observe an increase in land carbon uptake after an eruption in both models and the global carbon budget (GCB) data, though the models appear to underestimate the increase in the land carbon sink compared with GCB data from 2 years post-eruption onwards (Fig. ). On this basis, we conclude that the ESMs have a qualitatively realistic response to stratospheric aerosol injection by volcanoes (with the global land carbon sink tending to increase after explosive volcanic eruptions), but that the magnitude of this response appears to be slightly underestimated compared to the GCB data. This may be because this generation of ESMs does not routinely model the enhancement of photosynthesis by diffuse radiation . As a result, the results presented in this study are likely to be conservative with respect to the positive impacts of SAI on NPP and land carbon storage.

Figure  depicts differences between the SSP585 and G6sulfur scenarios by the end of the 21st century, where the hatching in these plots removes changes in land carbon storage that are not due to SAI from our analysis. See Fig. S2 for prescribed forest fraction changes between SSP585 and SSP245. In Fig. a there is an average global temperature decrease of approximately 2.2 °C in G6sulfur relative to SSP585, although this is not homogeneous across the globe. CESM2-WACCM, IPSL-CM6A-LR and UKESM1-0-LL all experience similar levels of global cooling, between 2.5 and 2.56 °C (Fig. ). CNRM-ESM1-2 meanwhile experiences less cooling, with a global average of 1.86 °C. Nevertheless, the level of cooling in the northern polar regions is similar to the cooling in CESM2-WACCM, IPSL-CM6A-LR and UKESM1-0-LL, because regions with less cooling, or even warming, are concentrated outside of the northern poles. MPI-ESM1-2-LR shows notably less cooling than even CNRM-ESM2-1 with global mean temperatures only 1.38 °C cooler, while the northern polar regions in MPI-ESM1-LR show little cooling, and warming of up to 2 °C in some regions. This warming may be caused by heating in the lower stratosphere around the tropics which results in surface warming in some northern high latitude regions, which can result in under-cooled polar winters and a reduced seasonal cycle in these regions . In general, the Northern Hemisphere cools considerably more than the southern, with polar regions in particular experiencing a cooling of 5 °C or more, while much of the ocean in the Southern Hemisphere only experiences decreases between 1 and 2 °C, a trend observed in most models (compare Figs.  and ).

SAI affects precipitation patterns compared to SSP585 (Fig. b) resulting in an average global decrease of about 6.4 %. The most pronounced changes occur around the equator, where a decrease in precipitation around the equator, and increases in the subtropics, are associated with a weakening of the Hadley cell. This weakening results from changes to the spatial distribution of aerosols due to SAI in G6sulfur . We also see evidence of a double ITCZ forming later in SSP585 projections compared to G6sulfur. A double ITCZ is a noted feature of many CMIP models in future climate projections and is acknowledged to be partly a consequence of model biases . Therefore, it is possible that this reduction in rainfall in the ITCZ region is in part simply due to no double ITCZ forming in the G6sulfur projections.

UKESM1-0-LL experiences the greatest drying of the models analysed in this study, on average drying by 0.26 mmd-1 globally. Meanwhile, MPI-ESM1-2-LR shows the smallest average global decrease in precipitation of 0.16 mmd-1. While this model still shows large decreases in precipitation around the equator, the mid to high latitudes show a notably smaller decrease in precipitation than the other models analysed here. All models show large decreases in precipitation over Indonesia of more than 1 mmd-1, while all models except CNRM-ESM2-1 show notable decreases in precipitation over central areas of the African continent (Fig. ). Going back to the ensemble mean (Fig. b), the effect of SAI is especially strong over the equatorial oceans, with smaller decreases spread across the mid to high-latitudes. Conversely, a statistically significant increase is observed over eastern Australia, where vegetation is largely water-limited . This region also shows a notable rise in NPP and land carbon storage (i.e. a greening under the G6sulfur scenario, Fig. c, d).

Relative to SSP585, global mean NPP increases in three models in G6sulfur (Fig. ). There is a global mean decrease of NPP in IPSL-CM6A-LR and MPI-ESM1-2-LR, associated with reduced precipitation, which can reduce NPP in water limited regions, and insufficient cooling, which can lead to plant temperatures going beyond the optimum for photosynthesis (see Table S1 in the Supplement). We also observe decreases in NPP (up to 0.2 kgCm-2yr-1) over large areas of the northern high latitudes in CESM2-WACCM where the model has the greatest cooling, though this is not visible in the global average due to counteracting increases in the Southern Hemisphere.

Global land carbon storage increases in all models except IPSL-CM6A-LR (Fig. ), which shows some areas of decrease in northern high latitudes and eastern Africa with little offset from land carbon storage increases in other regions. The only other model with a notable decrease in land carbon storage is MPI-ESM1-2-LR which has a region of decrease of more than 5 kgCm-2 in central regions of Africa. UKESM1-0-LL shows the greatest increase in land carbon storage of the models analysed, with a global average increase of 0.658 kgCm-2. These increases are concentrated around Amazonia, central Africa and south-east Asia, with smaller increases occurring around the mid to high latitudes.

The scenarios SSP245 and G6sulfur have similar global mean temperatures, but there are higher atmospheric CO2 concentrations under G6sulfur. This allows us to compare the relative impact of a given global warming achieved through conventional mitigation (SSP245) with that achieved through SAI (G6sulfur). Figure  shows equivalent maps to Fig. , but now for G6sulfur relative to SSP245. By design, the projected global warming is very similar. However, there is a residual warming of the polar regions in G6sulfur . This is most pronounced in MPI-ESM1-2-LR, which experienced increases of more than 4 °C in some northern polar regions, and is prominent in IPSL-CM6A-LR and UKESM1-0-LL (Fig. ). The simple strategy of injection at the Equator has been modified in other studies to reduce these residual impacts . IPSL-CM6A-LR produces the closest average global temperature in G6sulfur compared to SSP245 with a difference of only 0.016 °C. As this value is a global mean there are of course regional variations in the level of cooling achieved, with residual warming still being present in northern high latitudes in this model. The greatest difference between SSP245 and G6sulfur global average temperatures is observed in CNRM-ESM2-1 in which G6sulfur is 0.275 °C warmer than SSP245 by the end of the century, with lots of this warming concentrated over land masses. In particular, Amazonia and northern regions of Russia experience the largest area of warming between 2 and 3 °C compared to SSP245.

Global mean precipitation decreases slightly on average across the models (-3.7 %) (Fig. b), mainly due to a band of decreased precipitation around the Equator. This response is driven by a reduction in the intensity of the Hadley cell resulting from changes to the tropical distribution of aerosols associated with SAI in G6sulfur (Fig. ). Such regional changes in rainfall can have major regional impacts, an important consideration for SAI . A statistically significant (clear of stippling) increase in precipitation is also observed in eastern Australia, corresponding to increases in NPP and land carbon storage (Fig. c, d). Figure  shows that IPSL-CM6A-LR and UKESM1-0-LL have the greatest average decrease in precipitation globally in G6sulfur compared to SSP245. While UKESM1-0-LL appears to have more areas where precipitation decreases more than 1 mmd-1, it also has more areas in which precipitation increases notably, thereby offsetting the areas of decrease when the average is taken. Similarly, MPI-ESM1-2-LR has large areas that experience a decrease in precipitation of more than 1 mmd-1, but this is not necessarily reflected in the global average due to a substantial number of areas in which precipitation increases more than 0.6 mmd-1. In most models, there is a pattern of both large increases and decreases in precipitation concentrated in a band around the equator. Smaller changes in precipitation tend to occur in the mid-latitudes of the models.

Figure c depicts an increase in global mean NPP of about 15.6 % in G6sulfur compared to SSP245. Generally, areas with increased NPP in the ensemble mean coincide with agreement between models on the projected sign for this change. Much of the land in the Southern Hemisphere, southern Asia, and central America shows NPP increases in G6sulfur relative to SSP245. The larger NPP increases in G6sulfur, which are primarily due to additional CO2 fertilisation, drive a larger land carbon storage increase compared to SSP245 (+5.9 % as opposed to +2.8 %) (Fig. d). The largest increases in land carbon storage are observed in Indonesia, a difference that is not observable in the comparison between G6sulfur and SSP585 (Fig. d). All models show global mean increases in both NPP and land carbon storage in G6sulfur compared to SSP245 (Figs.  and ). CESM2-WACCM shows some decreases in NPP and land carbon storage in areas of eastern Europe, while IPSL-CM6A-LR has decreases of a similar scale in NPP and land carbon storage in northern Amazonia, Indonesia and central areas of the African continent. The increase in land carbon storage observed in IPSL-CM6A-LR, while the smallest of all the models, contrasts with the decrease in land carbon storage observed in the same model for the comparison with SSP585 (Fig. c). All other models display the same global upward trend in land carbon storage across both the SSP585 and SSP245 comparison. UKESM1-0-LL has the greatest average increase in NPP of the models analysed (0.0748 kgCm-2yr-1) compared to SSP245. Notable increases in NPP are largely focused around the equator and at lower latitudes, something reflected in the land carbon storage increases in UKESM1-0-LL (Fig. ). Conversely, the model which experiences the greatest increase in land carbon storage is CNRM-ESM2-1, which shows notable increases in land carbon storage across the globe, with the largest increases in the northern mid to high latitudes (Fig. ).

Figure  shows ensemble mean NPP and land carbon storage against global warming and CO2 concentrations. In G6sulfur, NPP exhibits a steeper increase with global warming compared to SSP245 and SSP585, which both track similar trajectories against temperature (Fig. a). Plotting NPP against CO2 shows overlapping trajectories across all three experiments, increasing steeply from pre-industrial levels before plateauing at around 800 ppm (Fig. b). The CO2 fertilisation effect plays a crucial role in these global mean responses, as has been shown in previous studies . Elevated CO2 levels in the SSP585 scenario yield a nearly 40 % increase in NPP compared to SSP245 (Fig. a). Conversely, global NPP shows minimal sensitivity to temperature, demonstrated in Fig. b where the change in total NPP in G6sulfur tracks SSP585 despite their different global mean temperatures, as a result of counteracting negative effects of warming in the tropics and positive effects in the high latitudes (Fig. ). G6sulfur and SSP585 show comparable global NPP increases relative to pre-industrial levels, consistent with global NPP being mainly dependent on CO2 concentrations. However, projected global land carbon storage increases are larger in G6sulfur relative to either SSP245 or SSP585 (Fig. c, d), but for different reasons.

Compared to SSP245, global land carbon storage is increased in G6sulfur due to extra CO2 fertilisation (Fig. c). This depends on the uncertain magnitude of CO2 fertilisation of photosynthesis, although it remains clearly visible even in the CMIP6 models that include nitrogen limitations. Meanwhile, global land carbon storage is increased in G6sulfur compared to SSP585 as the reduced warming in G6sulfur results in reduced soil respiration (Fig. d). These results build upon the findings of single model analysis on the impacts of SRM on land carbon storage and NPP , as well as analysis performed for idealised scenarios in which the solar constant is reduced . As far as global land carbon storage is concerned, G6sulfur therefore has the benefit of higher CO2 for photosynthesis (as in SSP585), but without the counteracting negative impact of additional warming compared to SSP245. The increases in global land carbon storage in G6sulfur relative to SSP585 occur despite a global decrease in precipitation and shifts in regional rainfall patterns within G6sulfur. Increased water use efficiency at higher concentrations of CO2, likely compensates for a large part of the reduction in rainfall . However, it is important to note that a reduction in precipitation has other impacts which, though not observable in NPP or land carbon storage, remain important to consider. Examples include threats to freshwater availability for human consumption and agriculture , increased susceptibility of soils to wind and water erosion and increased wildfire risk to name a few.

The ESMs analysed in this study do not fully represent all processes important to the carbon cycle. They do not, for instance, model the impact of topographical variation on groundwater and its impact on ecosystem productivity . These models also do not explicitly account for diffuse radiation and its impacts on the biosphere . However, we note that diffuse radiation fertilisation is expected to further increase land carbon storage under SAI. We also note that this study only analyses a small number of models for one SAI scenario, stratospheric aerosol injection, and that the risks and benefits of SAI depend strongly on how it is deployed . However, we see similar increases in projected NPP and land carbon storage in the G6solar experiments, which apply a reduction of the solar constant (Figs. S3 and S4). However, we see similar increases in projected NPP and land carbon storage in the G6solar experiments, which reduce the radiative forcing from the high emissions scenario (SSP585) to the medium forcing scenario (SSP245) by reducing solar irradiance (Figs. S3 and S4). Additionally, we note that SAI is a temporary mitigation measure which, while reducing global temperatures, does not address the primary drivers of climate change, (increased concentrations of greenhouse gases). As a result, if SAI is terminated, global temperatures would rise rapidly due to the elevated greenhouse gas concentrations (this is known as the “termination effect”, . Therefore, global efforts to reduce carbon emissions would still be required even if SAI were to be deployed temporarily to reduce the risk of Amazon dieback.

Model ensemble averages show that CMIP6 models tend to underestimate NPP in central and northeastern areas of the Amazon but overestimate in north-west and south easterly regions, when compared with MODIS data. Looking at individual models, CESM2-WACCM and IPSL-CM6A-LR underestimate NPP in Amazonia, while MPI-ESM1-2-LR generally overestimates NPP These individual model variations are important to take into account when interpreting CMIP6 outputs for the Amazon. Although improved on the CMIP5 model generation, the CMIP6 models still tend to overestimate temperatures in the Amazon basin , and underestimate rainfall . This tends to bias models towards less favourable conditions for the Amazon rainforest. However, CMIP6 models show greater agreement in projections of rainfall change in the Amazonian basin, than previous model generations .