Scientists estimate solar geoengineering risks with climate and Earth system models. They simulate a defined intervention—such as stratospheric aerosol injection, marine cloud brightening or cirrus cloud thinning—then track changes in temperature, rainfall, circulation, atmospheric chemistry, ecosystems and human systems. They test many deployment assumptions and compare simulations with observations, especially measurements from volcanic eruptions. The models indicate that some interventions could lower global average temperature, but they do not provide a precise, predictable regional climate outcome or remove the underlying risks of greenhouse-gas warming.
What scientists mean by solar geoengineering
Solar geoengineering, also called solar radiation modification (SRM), refers to deliberately changing how much solar energy Earth absorbs or how much heat escapes to space. The objective in most studies is to offset part of the warming caused by greenhouse gases. It would not remove carbon dioxide or other greenhouse gases from the atmosphere.
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The National Academies of Sciences, Engineering, and Medicine’s Reflecting Sunlight: Recommendations for Solar Geoengineering Research and Research Governance (2021) identifies three leading atmospheric concepts. They act at different altitudes and through different physical processes, so a result for one cannot simply be transferred to another.
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →| Method | Physical mechanism | Where it acts | Typical modeled pattern | Important uncertainties |
|---|---|---|---|---|
| Stratospheric aerosol injection (SAI) | Adds reflective particles that scatter some incoming sunlight. | Stratosphere, above most weather. | Potentially broad, near-global cooling, with regional differences determined by placement, particle properties and atmospheric circulation. | Aerosol chemistry, particle size and lifetime, ozone effects, circulation responses and regional precipitation. |
| Marine cloud brightening (MCB) | Attempts to make some low marine clouds more reflective. | Marine boundary-layer clouds. | More geographically concentrated effects and potentially stronger spatial contrasts. | Cloud formation and aerosol–cloud interactions are represented weakly in many models; the achievable response is uncertain. |
| Cirrus cloud thinning (CCT) | Changes high-altitude ice clouds so that more outgoing heat escapes. | Upper troposphere, in cirrus clouds. | Climate response depends strongly on where and when cirrus changes occur. | Ice-cloud microphysics, transport, feedbacks and the net regional hydrological response are poorly constrained. |
How the modeling process works
A study does not place a geoengineering system in the real atmosphere. It represents an intervention as a change in radiative forcing or in the properties of aerosols or clouds, then calculates how the simulated climate responds. The sequence normally includes the following decisions and tests.
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1. Define the intervention and its assumptions
Researchers specify the material or cloud change, the altitude or region, the season, the amount introduced, and how long the intervention continues. For SAI, assumptions include particle composition, size distribution, injection latitude and altitude, and whether emissions are steady or adjusted over time. For MCB and CCT, researchers must describe where cloud properties are altered and how large an area is affected.
These choices matter because the same total radiative forcing can produce different circulation, precipitation and chemical responses when it is distributed differently. A model run therefore represents a scenario, not a single universal geoengineering result.
2. Calculate the initial radiative forcing
The model first estimates how the intervention changes incoming sunlight, reflected sunlight or outgoing infrared heat. In an SAI experiment, particles scatter and absorb radiation while being transported by stratospheric winds. In MCB, cloud droplet number and reflectivity alter the amount of sunlight reflected back to space. In CCT, modified ice clouds change the balance between reflected sunlight and outgoing long-wave radiation.
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3. Let the climate system respond
Atmosphere, ocean, land surface, sea ice and—in Earth system models—carbon-cycle components exchange energy and matter at each time step. The simulation calculates temperature, winds, humidity, clouds, precipitation, ocean currents and other variables as the forcing interacts with climate feedbacks. Researchers run the intervention against a comparable scenario without it, often using multiple initial conditions or model versions to create an ensemble.
4. Examine more than global temperature
Global mean surface temperature is an important diagnostic, but it is only one output. Scientists also inspect regional temperature, rainfall and runoff; atmospheric circulation; drought and heavy-precipitation indicators; ozone chemistry; direct and diffuse sunlight; ocean changes; carbon-cycle responses; extreme events; and potential effects on ecosystems, agriculture and public health.
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The National Academies summarizes the role of these tools this way: “Climate (or Earth system) models are the critical tool to assess feedbacks and mechanism-specific changes associated with SG intervention strategies.” The quotation is from the National Academies’ 2021 report, Chapter 2.
5. Vary scale, location and duration
Scientists repeat simulations with different intervention strengths, geographic patterns and start or stop dates. The purpose is to identify outcomes that are robust across plausible choices and outcomes that depend on a particular setup. A model showing a benefit under one injection latitude, for example, does not establish that the same benefit would occur elsewhere.
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What volcanic eruptions teach—and what they cannot prove
Large eruptions provide the most important natural evidence for the basic physics of aerosol cooling. Sulfate particles released into the stratosphere can spread around the globe, reduce incoming solar heating and cool the surface. The National Academies’ 2021 assessment, citing the Intergovernmental Panel on Climate Change (IPCC, 2013), reports approximately 0.5°C of global mean cooling for one year or more after the 1991 Mount Pinatubo eruption.
That figure is evidence that aerosol-driven cooling is physically possible; it is not a forecast for a deliberate SAI program. A volcano releases material in a particular place and season, with a composition and particle-size distribution that may differ from an engineered release. Volcanic aerosol burdens usually decline over a few years, whereas a deliberate intervention could be maintained for decades. Background greenhouse-gas concentrations, atmospheric chemistry and circulation would also differ.
Scientists compare satellite and ground-based measurements of aerosol optical depth, radiation, temperature and chemistry with model output. A match to one observation is not enough: the National Academies notes that models can reproduce an observed aerosol optical depth through compensating errors while representing the underlying processes incorrectly. Researchers therefore seek agreement across several observations and process-level measurements.
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Can climate models predict what solar geoengineering would do?
Models can estimate physical responses under stated assumptions, but they cannot deliver a single guaranteed regional forecast for a technology that has never been deployed at full scale. Confidence is generally higher for the direction of the global radiative effect than for local rainfall, storm tracks or ecosystem consequences.
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Many clouds, aerosol particles and precipitation processes occur at scales smaller than a global model grid. Models represent those processes with parameterizations—mathematical approximations calibrated against observations and higher-resolution studies. The National Academies says that MCB and CCT processes are especially poorly represented in current climate models. SAI also depends on parameterized aerosol microphysics, transport and chemistry.
Ensembles and scenario spread
Researchers use ensembles to separate internal climate variability from the intervention signal and to test sensitivity to model structure. Results can diverge because models use different cloud schemes, ocean resolutions, aerosol treatments or assumptions about the intervention. A wide spread is not a reason to select the most favorable run; it identifies a risk or decision that remains unresolved.
Downscaling for regional information
Global simulations are too coarse for many water, agricultural and infrastructure decisions. Researchers therefore use regional models or statistical downscaling to add local detail. Downscaling cannot create information that the global model lacks: if the large-scale circulation response is uncertain, the local projection inherits that uncertainty. The National Academies calls for improved downscaling of climate and ocean projections, along with better observations of clouds and precipitation.
Which risks are examined in the simulations?
Regional temperature and precipitation
Cooling the global average does not restore the climate that would have existed without greenhouse-gas emissions. Greenhouse gases remain in the atmosphere, and the intervention changes the energy balance through a different mechanism. Models therefore find residual regional temperature differences and geographically uneven precipitation changes.
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Hydrological effects are particularly important because rainfall responds to circulation, cloud processes and atmospheric heating, not only to the global temperature average. Some areas could receive less precipitation while others receive more. The National Academies cautions that these regional effects are highly uncertain and depend on the intervention method and deployment choices.
Atmospheric chemistry and ozone
Stratospheric particles can participate in chemical reactions that affect ozone, while absorbing particles can heat parts of the stratosphere and alter circulation. Models track these chemical and dynamical pathways rather than treating aerosols as a simple sunshade. The size, composition and altitude of particles influence both the climate response and the potential ozone response.
Light and photosynthesis
Aerosols can change the balance between direct sunlight and diffuse light. Total sunlight may fall while the proportion arriving as diffuse light rises, with consequences that vary by cloud cover, latitude, vegetation and season. Earth system studies examine these changes because crop and ecosystem productivity cannot be inferred from temperature alone.
Oceans and the carbon cycle
Surface cooling, altered winds and changed rainfall can affect ocean stratification, circulation and carbon uptake. Earth system models follow these interactions over years to centuries. They do not treat the ocean as a passive heat reservoir, and they test whether carbon-cycle feedbacks amplify or dampen the intervention response.
Ecosystems, agriculture and extremes
Researchers translate climate outputs into exposure indicators for ecosystems and food production, including temperature thresholds, water availability, growing conditions and extreme events. A global average benefit can coexist with local stresses if rainfall patterns, sunlight or seasonal temperatures shift in unfavorable ways.
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Comparing SAI, MCB and CCT in risk studies
| Comparison axis | SAI | MCB | CCT |
|---|---|---|---|
| Primary forcing | Reflective stratospheric aerosols reduce absorbed sunlight. | Brighter low marine clouds reflect more sunlight. | Altered high cirrus changes outgoing heat and reflected sunlight. |
| Spatial reach | Potentially global after transport, though the pattern depends on injection strategy. | More regional and ocean-focused, with potentially strong contrasts between affected and unaffected areas. | Depends on the location and season of cirrus modification. |
| Most scrutinized physical uncertainties | Aerosol size, chemistry, transport, ozone and circulation. | Cloud formation, aerosol–cloud interactions and precipitation responses. | Ice-cloud microphysics, cloud feedbacks and net radiative effect. |
| Regional hydrological concern | Uneven rainfall and circulation changes despite global cooling. | Potentially concentrated rainfall and circulation changes near intervention regions. | Uncertain precipitation and circulation effects because cirrus processes are weakly represented. |
| Stopping the intervention | Requires explicit termination scenarios because greenhouse-gas warming remains. | Would remove its local forcing, with consequences dependent on the affected region and background warming. | Would remove its forcing, with consequences dependent on the intervention pattern. |
The table describes how scientists frame comparisons; it does not rank one method as safe. The methods alter different parts of the climate system, and uncertainty in one column cannot be used to fill gaps in another.
What happens if solar geoengineering suddenly stops?
Termination is modeled as a conditional scenario, not an observed event. If an intervention had been offsetting part of greenhouse-gas warming and then stopped abruptly, the cooling influence would disappear while greenhouse gases remained. Research summarized by the Congressional Research Service (2023) indicates that SAI termination could produce rapid warming toward the temperature that would have occurred without the intervention.
The speed of that transition matters for ecosystems, agriculture, infrastructure and human health. Species and societies might have adapted to the managed climate state and then face a compressed period of warming. Models test different intervention durations and stop dates to estimate how quickly temperature and precipitation could change. They cannot determine the political or operational likelihood of an abrupt stop, which depends on future institutions and deployment decisions.
Why uncertainty is a result, not a footnote
Uncertainty comes from several distinct sources:
- Physical uncertainty: incomplete knowledge of aerosol, cloud, radiation and precipitation processes.
- Model uncertainty: different model structures and parameterizations produce different responses.
- Scenario uncertainty: the material, location, amount, timing and duration of an intervention are unknown.
- Internal variability: natural climate fluctuations can mask or exaggerate an intervention signal in a finite simulation.
- Impact uncertainty: translating climate variables into crop, ecosystem, health or economic consequences requires additional models and assumptions.
A model ensemble is most useful when it shows which conclusions persist across these uncertainties and which do not. Reporting only the ensemble mean can hide a wide range of regional outcomes.
What models do not establish
- They do not prove that a full-scale intervention would be safe.
- They do not establish a precise local rainfall outcome for a future deployment.
- They do not show that cooling would reverse ocean acidification or other effects caused by elevated carbon dioxide.
- They do not remove the need to cut greenhouse-gas emissions and adapt to impacts already underway.
- They do not settle governance questions such as who chooses the target climate, who bears regional risks or how an intervention could be monitored and stopped.
The United Nations Environment Programme’s 2025 issue note, Solar Radiation Modification: No substitute for real climate action, emphasizes that SRM does not reduce greenhouse-gas concentrations or itself address climate impacts. It also highlights modeled concerns about uneven regional hydrology, disruption of climate patterns, biodiversity and the ozone layer.
How to read a solar-geoengineering modeling claim
- Identify the method. A result for SAI, MCB or CCT is not automatically transferable to the others.
- Check the forcing and deployment assumptions. Look for altitude, latitude, material, intensity, season and duration.
- Separate global and regional results. A global-mean temperature number says little about a particular watershed or crop region.
- Look for precipitation and circulation outputs. These often carry more regional uncertainty than temperature.
- Ask how observations constrain the model. Volcanic records, satellites and in-situ measurements test different parts of the simulation.
- Check the model limitations. Cloud, aerosol and precipitation parameterizations can dominate the uncertainty.
- Look for termination experiments. A study that omits stopping scenarios does not answer the full risk question.
- Keep emissions in the baseline. SRM changes incoming or outgoing energy; it does not remove the greenhouse gases driving long-term warming.
Bottom line for readers
Scientists model solar geoengineering by combining radiative-forcing experiments with coupled climate and Earth system simulations, then testing the outputs against observations and natural analogues. The evidence supports the physical possibility of global cooling, especially for aerosol-based approaches, but it also shows why cooling is not equivalent to restoring a preindustrial climate. Regional rainfall, circulation, ozone, ecosystems, sunlight and the consequences of stopping remain central uncertainties. Modeling can map those conditional risks and identify research priorities; it cannot turn an untested planetary intervention into a predictable or risk-free substitute for cutting emissions.
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