This study examined various mitigation and geoengineering experiments to understand these strategies’ potential impacts and identify the most feasible solutions to address global climate change. Some of these experiments included CO2 removal, setting emissions targets, and employing solar dimming, SAI, and cloud seeding technology to reduce solar forcing; these operations and simulation outputs were instantiated and simulated with the UKESM1-0-LL model and compared with ERA5 reanalysis data. In the near term, solar geoengineering strategies could accomplish the brief objective of allowing human management of the solar constant. However, these methods must account for lagged effects from bias and uncertainty originating from Earth system complexities and innumerable multiscale biogeochemical processes. Moreover, potential unintended consequences resulting from each of these geoengineering scenarios are essential to highlight; in particular, Tsurf and P briefly reach – and exceed – stabilization thresholds during the contemporary period in many of these experiments, with rapid warming and increased P, bolstering an enhanced runaway greenhouse effect.
Traditional mitigation approaches primarily focus on reducing GHG emissions to stabilize global temperatures, afforestation, carbon aerosol capture, biochar, and ocean fertilization1,46. Alternatively, market-based instruments include carbon offset incentivization and global carbon accounting approaches (e.g., renewable energy credits). However, current efforts are insufficient to achieve the 1.5 °C climate stabilization target, with global emissions expected to exceed critical thresholds within the next decade. Mitigation strategies often underestimate feedback mechanisms and systemic uncertainties, limiting their effectiveness in stabilizing Earth’s climate systems. Mitigation simulations (e.g., 1pctCO2-cdr) demonstrate reduced Tsurf and altered P patterns in the short term. However, Arctic amplification persists, with pronounced warming in high-latitude regions despite aggressive CO2 reductions. These strategies generally promote gradual stabilization but cannot prevent erratic climate responses entirely.
In contrast, geoengineering involves direct, large-scale manipulation of the Earth’s climate system with SRM and carbon-focused interventions. SRM strategies such as the G6Sulfur sub-experiment demonstrate significant short-term cooling effects while introducing long-term risks, e.g., uneven precipitation patterns facilitate ecosystem disruption, Arctic warming, and equatorial cooling. Other SRM intervention models – including the G7Cirrus sub-experiment – predict marginal Tsurf reductions but highlight P and [CH4] variability, underscoring the complexity of managing interdependent climate systems. Long-term simulations reveal that intervention strategies might exacerbate feedback loops or introduce irreversible climatic shifts when improperly managed. Alternatively, geoengineering intervention via carbon management, including ocean fertilization and enhanced mineral weathering, aims to increase carbon uptake but often encounters substantial ecological uncertainties and unintended consequences, such as hydrological cycle disruption, ocean acidification, biodiversity loss, and ecosystem collapse.
Traditional mitigation strategies offer gradual and predictable outcomes, while geoengineering interventions promise rapid temperature reductions but introduce substantial risks and uncertainties. Mitigation strategies are generally global in scope and balanced in effect, while geoengineering exhibits pronounced regional anomalies (e.g., Arctic warming and equatorial cooling). Mitigation strategies align with natural processes and are more sustainable over the long term. Conversely, geoengineering often requires sustained intervention to maintain results, as abrupt cessation can lead to catastrophic climatic rebound. Geoengineering introduces unknown risks and ethical considerations, such as governance and global equity in impacts, while mitigation emphasizes systemic changes to reduce the root causes of climate change. While mitigation offers long-term sustainability and aligns with global climate stabilization goals, geoengineering provides a rapid but risky pathway to counteract warming trends. Effective climate strategies will likely require an integrated approach, balancing mitigation with carefully regulated geoengineering interventions to address the multifaceted challenges of climate change.
Collectively, intervention and mitigation simulations tended to overestimate the variability and magnitude of Tsurf and P, with substantial regional deviations and scenario-dependent estimation heterogeneity for [CH4]. This highlights the challenges in accurately quantifying and projecting GHG feedback mechanisms. Furthermore, forward projections indicate that both mitigation and intervention scenarios can lead to varied climate responses, emphasizing the complexity and uncertainty in predicting the exact outcomes of different geoengineering strategies. This suggests that model uncertainty may promulgate through the projections but represents a near-to-observational record that is still useful for forecasting. These results demonstrate the efficacy of employing systematic physics-based data-driven methodologies to extend temporal envelopes for exploring multimodal intercomparisons and applying sensitivity analyses to better understand Earth system complexities and highlight potential limitations and unintended consequences of mitigation and intervention strategies. Future efforts will introduce artificial intelligence optimization to enable knowledge discovery and bolster confidence for recommendation and contingency formulation.
Although imperfect compared to observational records, these mitigation and intervention simulation experiments capture large-scale Earth system dynamics well; however, some discrepancies emerge. Utilizing global mean Tsurf and P as points of departure – with the understanding that regional-to-localized dynamics still require more spatially explicit refinement in Earth System Model frameworks – reanalysis and model intercomparisons act as quality assessments and validation baselines to inform calibration updates (i.e., forcing observations, parameterization, drivers) and improve future simulations47. Each model run exhibited unique responses to experiment-specific mitigation (1) or intervention (2) strategies; most importantly, no experimental ‘solution’ produced a stable, steady-state Earth system at any point in time. By introducing geoengineering strategies into the baseline environment, rapid and sustained changes to the Earth system are observed.
1. Mitigation scenarios were simulated to demonstrate the potential of CO2 removal strategies to alter global climatologies in the future substantially; in particular, initialization of the “1pctCO2-cdr“1pctCO2-cdr experiment resulted in global mean climatologies demonstrating consistent increases in Tsurf, P, and [CH4] from 1990 to 2149 (3.89\(\:\pm\:\)2.62\(\:^\circ\:\)C, 0.17\(\:\pm\:\)0.78 mm day-1, 0.01\(\:\pm\:\)0.27 ppb). In contrast, Tsurf and P were reduced while [CH4] increased from 1990 to 2022 (-0.01\(\:\pm\:\)0.37ºC, -0.02\(\:\pm\:\)0.89 mm day-1, 0.02\(\:\pm\:\)0.25 ppb), suggesting an effective cooling and drying mechanism in the short-term yet vastly underestimates the evolution of Tsurf and P (4.16\(\:\pm\:\)0.05ºC, 0.98\(\:\pm\:\)0.01 mm day-1). Critically, this experiment precipitated pronounced Arctic amplification, with Tsurf in excess of 13.08 °C across the tundra and boreal ecotones. In addition, the 1b esm-1pct-brch-1000PgC experiment demonstrated a more balanced climate response with less pronounced increases in global Tsurf, P, and [CH4] from 1950 to 2149 in comparison to 1a, i.e., 0.06\(\:\pm\:\)0.35ºC, 0.003\(\:\pm\:\)0.331 mm day-1, 0.001\(\:\pm\:\)0.169 ppb. However, relative to the entire simulation period, regional variabilities resulted in Tsurf reductions for the 1990–2022 simulation period, with marginal increases in P and [CH4] trajectories (-0.03\(\:\pm\:\)0.30\(\:^\circ\:\)C, 0.001\(\:\pm\:\)0.330 mm day-1, 0.01\(\:\pm\:\)0.16 ppb). The “esm-1pct-brch-1000PgC” experiment indicates that even under aggressive CO2 removal and carbon management strategies, complex climate feedbacks remain unfettered48.
We extended the distribution curve tails toward a ‘new normal’ by increasing CO2, seemingly magnifying contemporary observed changes of punctuated extreme weather and precipitation49. This variability introduces shifts in regional climates, though it may also be responsible for the increased global warming trend. While the ranges of Tsurf and P in esm-1pct-brch-1000PgC is less than 1pctCO2-cdr, P is increasingly variable in the models, with interannual shifts in P patterns across regions. While the variability of the models at a regional scale may account for some of these outputs, the overall trend towards punctuated P, governed by biogeochemical processes that are not fully captured by the models, is of concern. Moreover, extending the “1pctCO2-cdr” experiment beyond the 1000 PgC benchmark facilitates increasing loss of [CH4] beyond net zero, which may have biogeochemical consequences over time. Regional incongruencies in Tsurf and P are localized to hot and cool dry ecotones exhibiting high net primary productivity and persistent carbon sink-to-source conversion.
2. Various intervention scenarios were explored with numerous geoengineering simulations analyzed, including SRM.
strategies (e.g., solar dimming, stratospheric aerosol injection, cirrus cloud thinning), with many demonstrating the potential to significantly modify Tsurf, P, and [CH4] patterns with varying regional impacts. The G1 experiment (i.e., 4xCO2 and solar dimming) illustrates global radiative balance by reducing solar irradiance to stabilize climatological feedbacks, resulting in significant Tsurf modulations (i.e., slight cooling), extensive variability in P, and increasing [CH4] across the simulated period, i.e., 1850–1949 (-0.01\(\:\pm\:\)0.47\(\:^\circ\:\)C, -0.01\(\:\pm\:\)0.32 mm day-1, 0.003\(\:\pm\:\)0.180 ppb). However, regional hotspots were distinguishable, with pronounced warming trends and emergent climatic patterns, trends, and anomalies. In the “G6Solar” G6Solar experiment, high-to-medium solar forcing reduction promoted cooling trends, with elevated Tsurf and [CH4] hotspots despite an overall decrease in global mean variability (i.e., departures). Despite reducing Tsurf, P, and [CH4] from 2020 to 2022 (-2.55\(\:\pm\:\)2.61\(\:^\circ\:\)C, -0.08\(\:\pm\:\)0.61 mm day-1, -4.37\(\:\pm\:\)0.62 ppb) – and cultivating cool, dry spots across the globe in the short-term – Tsurf, P, and [CH4] rebounded and increased over the 2020–2100 simulation period (1.28\(\:\pm\:\)1.26ºC, 0.05\(\:\pm\:\)0.35 mm day-1, 1.58\(\:\pm\:\)1.08 ppb). The “G6Sulfur” experiment (G6Sulfur) aimed to reduce radiative forcing via SAI methods and demonstrated promising results to mitigate warming and cool the planet in the short-term but reduced global P departures (i.e., variability) by doing so. Additionally, the results suggest that this strategy may cause abrupt climatic changes and pose significant long-term stability issues beyond the initial beneficial impacts being achieved, and aerosol effects are diminished. Similarly, the “G7Cirrus” experiment (G7Cirrus) initially facilitated an increase in Tsurf and [CH4], though regional patterns of reduced Tsurf variability were observed. In the long term, the impacts on P variability are significant. Collectively, these results suggest a complex interplay of oscillating dynamics between global, regional, and localized climate regimes and highlight the challenges in forecasting accurate outcomes under mitigation and intervention mechanisms.
“G6Solar” simulation outputs of global weighted mean variabilities indicate synergistic coregulated feedbacks, with persistent trends characterized by abrupt pulses; specifically, [CH4] began and ended the simulation period with net emissions in terms of annual magnitude, but throughout most of the simulation period, [CH4] variability decreased and remained low. In contrast, P variability illustrated anticorrelated behavior, i.e., the simulation period began and terminated with minimal precipitation events and accumulation but rather a steady increase followed by a gradual decrease – in enhanced P variability. Tsurf characteristically stimulates climate feedback patterns governing water vapor distribution and precipitable accumulation. During this validation window, Tsurf and P differencing and dimensionality reduction via spatial flattening demonstrates that over regions with high variability in P, Tsurf departures were marginal and remained stable (i.e., oceans, equatorial tropics); however, less P variability prompted an elevation of Tsurf variability, triggering positive climate feedbacks (i.e., high latitudes). These trends remained periodic with gradual increases in Tsurf and P – in concert with net release yield from [CH4] – through the end of the century, followed by a slight stabilization period.
Marked reductions in global mean departures from “G6Solar” simulations suggest this strategy will cool the planet while synergistically amplifying the positive coupling feedbacks between Tsurf and P, fostering cool, drier conditions. Examining global climatologies and mean departures for each of the covariates indicate similar characteristics to prescribed solar geoengineering scenarios, with marked increases in global weighted mean variability for P variability across the entirety of eastern Brazil – from Bahia to São Paulo – as well as Tsurf and [CH4] anomalies near the Ross and Amery Ice Shelf. Results from these simulations are shockingly similar to those derived from the previous experiment; however, when constraining and comparing these dynamics with a three-year period and examining global response, subtle nuances between experimental methods are blanketed by statistical liberties. These nuances are not readily discernible in the second panels illustrated in Figs. 5 and 6, both yielding nearly identical visual outputs; however, it is important to note that this three-year window is a sensitivity method for validation purposes and successfully demonstrates similar spatiotemporal dynamics with those observed and re-analyzed datasets. Moreover, the global mean variability between the two experiments from 2020 to 2100 results in strikingly different outcomes, thus illustrating how climatologies change, anomalies evolve, and land-ocean-atmospheric interactions over space and time foster global climate change. Tsurf variability in “G6Solar” is likely coupled with unique surface flux exchanges and biogeophysical dynamics. However, it is more likely associated with the erratic variability of P. SAI, which has the potential to mitigate solar irradiance within Earth’s atmosphere for a finite amount of time. The model results respond accordingly, with steady, linear, and increasing Tsurf and P for the first 30 years after initialization, followed by an abrupt increase in global mean Tsurf. These dynamics may be caused by feedback interactions with the Earth systems or due to progressive atmospheric sulfate loss. Still, the stabilization of the system with abating temperatures, even in the absence of fossil fuel reductions, is concerning, especially when abrupt increases occur. This strategy exhibits less.
interannual variability and challenges long-term climate stability.
Relative to baseline simulations, SAI methods facilitate similar trends to these patterns. However, the magnitude of P’s global weighted mean variability is significantly higher as a consequence of G6Solar, while Tsurf exhibits lower variability. In addition, “G1” and “G6Solar” produced anticorrelated covariate trends, with G1 showing higher variability of Tsurf, P, and [CH4]. This is due to solar irradiance reduction techniques. The G1 experiment created a broad reduction in variable magnitude but demonstrated how reduced variability does not necessarily foster and maintain Earth system stability. This experiment yields a poorly distributed and relatively static lower atmosphere with less fluidity and more turbidity (i.e., amplified hot and cool dry regions, Antarctic methane pooling). These two approaches address and resolve uncertainty challenges by altering light or matter. Another case of an anticorrelated covariate trend is G6Sulfur and 1pctCO2-cdr: “G6Sulfur” induces a reduction in Tsurf and [CH4] variability while increasing P variability on a global scale. In contrast, 1pctCO2-cdr increases Tsurf variability coupled with a less variable global weighted mean in P. The variability of the global weighted mean of [CH4] was significant during G6Sulfur, representing nearly four orders of variable magnitude relative to [CH4] output variability from 1pctCO2-cdr. The dissimilar system responses reiterate that small perturbations can have significant effects when magnified across space and time. During the G7Cirrus experiment, the global mean Tsurf is reduced considerably. The elevation in P may be marginal, but the variance has a long tail toward positive, which could result in considerable precipitation increases over time. The mean cooling response from this experiment requires more time than the others, reaching the maximum radiative and cooling benefit over 160 years.
Based on the observed record, model simulations, sub-experiment intercomparisons, and statistical analyses, potential mechanisms and drivers of change are explored and elucidated within spatiotemporal patterns, relationships, and trends. First, warming departures in Greenland and northern Siberia facilitate land-atmosphere interactions through arctic amplification due to reductions in snow and ground ice, subsequent albedo reduction, and coincident amplification of solar energy absorption and land Tsurf. Hot spot anomalies – in particular, Tsurf departure anomalies – may be a byproduct of equatorial and polar temperature gradient reductions contributing to a weakening of the polar jet stream resulting from the reduction of the temperature gradient. With amplified surface warming and accelerating permafrost degradation, a higher rate and magnitude of [CH4] release from thawing permafrost contributes directly to localized weather patterns. [CH4] departures are located in model diagnostics to isolate and, more explicitly, identify drivers of regional warming. We quantify albedo changes and feedback amplification using remote sensing and process-based models to disentangle driving mechanisms and reconcile algorithm, data, and knowledge gaps.
Furthermore, SRM deployment may alter the cloud cover and radiative balance in high-latitude ecotones due to immediate cooling effects. Alternatively, warming departures in the Horn of Africa may facilitate ocean-atmosphere interactions in the form of alterations to sea surface temperatures (SSTs) in the Indian Ocean, disrupt monsoon systems, and catalyze warmer weather with sporadic precipitation activity. Demonstrating the validity of this conjecture would require coupled oceanic and atmospheric models to assess an SST-driven impact on regional climatological variability. Additionally, persistent drought events were observed and simulated in this region; these disturbances and conditions elevate surface albedo via soil desiccation, reducing latent heat flux and promulgating heat intensification. Moreover, model parameterization, including soil moisture and vegetation feedbacks, would enhance the model architecture, increase the precision of the simulation outputs, and reduce uncertainties and confidence intervals that directly validate and support drivers of change.
Cooling mechanisms and drivers of change are further examined; in particular, central Africa and eastern Brazil experienced significant regional cooling effects. Drivers contributing to regional cooling departures in central Africa may result from vegetation feedbacks and changes to the hydrological regime; more specifically, afforestation catalyzes an increase in evapotranspiration and latent heat flux, effectively cooling Tsurf. Though enhanced forest cover may be a minute contributor, this hypothesis may be validated by integrating dynamic vegetation models into the coupled terrestrial and atmospheric circulation models to simulate land-atmosphere interactions while conducting cooling effect assessments effectively. In contrast, moisture transport variability may result from altered trade wind patterns, thereby increasing P and promulgating evaporative cooling; similarly, this hypothesis may be tested with remote sensing platforms that observe water vapor flux and cloud cover variability. Alternatively, the historical rate and magnitude of deforestation in eastern Brazil across the Amazon basin may help contribute to localized cooling patterns via reduced transpiration and latent heat flux (i.e., land-use land-cover change scenarios may be used to emulate and assess the interplay among the spatiotemporal dynamics of deforestation and Tsurf trends across this region). In addition, cooling effects in eastern Brazil may be a result of the circulation pattern of the Atlantic Ocean; more specifically, disruptions to the Atlantic Meridional Overturning Circulation (AMOC) would drastically affect heat transport to the tropics. Simulating this scenario with AMOC variations in response to various geoengineering intervention strategies would validate and effectively constrain potential states of the regional climate.
Discrepancies between the observational record, model simulations, and statistical products are present, most notably acknowledged previously as over- and under-estimations of simulated outputs and observed data. In particular, P was overestimated in southern Alaska, while P was underestimated in east Africa. First, we expound on potential climatological, geophysical, and biogeochemical contributors to these errors; then, we expand these inquiries to technological limitations. The error or overestimation of P in southern Alaska may be a result of marine-dominated regimes; in particular, warm SSTs in the North Pacific may expedite a significant amount of moisture and heat transport to southern Alaska (i.e., atmospheric river), prompting the development of thunderstorms and excessive precipitation. Alternatively, if warm SST in the North Pacific does not fuel the system, model simulations and architectures may not accurately reflect orographic effects or convective processes. Instead, much of the overestimation error and biases likely originate from inadequately accounting for and capturing the dynamics of cloud microphysics and convective processes. To remedy this, adapting high-resolution cloud-resolving models into the coupled oceanic and atmospheric circulation models to better resolve and improve the representation of orographic P.
The underestimation of P in East Africa may be a byproduct of generalized downsampling; however, the inability to capture and simulate localized convection and mesoscale phenomena at coarse resolution may contribute iteratively to the resulting error and underlying biases. Furthermore, this underestimation of P may result from poor representation of monsoon dynamics in simplified parameterizations of moisture convergence and seasonal wind shifts. Integrating observational datasets for model calibration purposes would reduce errors and biases significantly. Broader structural constraints and model limitations may directly contribute to these P errors via parameterization errors and feedback mechanisms; however, spatial resolution was not a contributing factor in this context (i.e., preprocessing methodology guided by ERA5 resolution). By refining parameterizations, updating simplified assumptions generalizing cloud formation, radiation, and convection – and interjecting complexity with satellite cloud retrievals and ground-based radar data – regional climate simulations will contain fewer biases and output more precise data products. Amending the represented feedback mechanisms in coupled Earth system model frameworks is crucial as well to enhance the predictive accuracy of the simulations (e.g., vegetation-climate, ocean-atmosphere).
Climate change remains a critical issue, and the likelihood of geoengineering intervention in the future is likely. Geoengineering represents an unprecedented scale of planetary intervention, carrying profound implications and the risk of significant consequences. It is a field marked by considerable uncertainties that can only be addressed through systematic and coordinated research efforts. In addition, while CDR strategies are integral to many climate change mitigation scenarios, their development has yet to achieve the necessary deployment scale required for climate change mitigation, and their impacts on the Earth system still need to be better understood. Through detailed simulation analyses, this research study – presuming other approaches to this challenge are generalized and not comprehensive – identified substantial variability in global, regional, and localized climate patterns from ERA5 reanalysis observations over 73 years (i.e., 1950–2022) as well as six mitigation and intervention simulations from CMIP6 experiments (i.e., CDRMIP, GeoMIP) from 1850 to 2149, reflecting complex hydrological responses to changing atmospheric conditions. While the experiments provide valuable insights into potential strategies for managing climate change, they also reveal the complexities and uncertainties involved, necessitating further research and cautious approaches to large-scale implementation of geoengineering solutions.
Geoengineering strategies are intrinsically characterized by significant uncertainties, those which critically govern and influence decision-making processes by shaping the evaluation of risks, trade-offs, and potential unintended consequences. Empirical, temporal, technological, ethical, and socioeconomic uncertainties permeate from various stages of geoengineering deployment, from regional climate responses to governance and moral challenges. Ethical and governance issues arise from these uncertainties, significantly influencing international cooperation and policy frameworks. Intervention often produces unequal regional impacts, e.g., the G7Cirrus sub-experiment, which illustrates increased P variability in equatorial regions but induces drying patterns in parts of southeast Asia and Brazil. Quantitative evaluations, including the \(\:\pm\:\)1Wm-2 radiative forcing reduction in cloud-seeding sub-experiments, inform the design of equitable governance frameworks to address these disparities. Additionally, uncoordinated regional deployment of cloud-seeding efforts highlights the risks and pressing need for global regulatory mechanisms. While cloud-seeding experiments suggest radiative forcing reductions of 50–85%, geographic variability in outcomes necessitates comprehensive oversight. Uncertainties from geoengineering outcomes arise due to an incomplete understanding of Earth system processes, model limitations, and potential unintended consequences. Model simulations such as the G6Sulfur experiment illustrate erratic regional climate responses, with P departures ranging from − 2.22 mm day-1 to 3.13 mm day-1 globally. Regions such as eastern Brazil experience extreme drying, while high-latitude areas exhibit increased P variability, complicating regional projections. Similarly, [CH4] dynamics and sensitivity to atmospheric chemistry variability add to the complexity; G6Sulfur simulations indicate global mean [CH4] variability between − 4.43 ppb and 6.38 ppb, raising concerns about activation and amplification of nonlinear feedback loops, particularly in methane-sensitive regions. These regional disparities compel policymakers to weigh localized risk against global cooling benefits, underscoring the cautious approach policymakers adopt. This complicates large-scale deployment consensus and includes the implementation of well-informed decisions and actionable task delegation that minimizes risk and maximizes utility and efficiency, ultimately seeking to diminish feedback-driven warming while prioritizing adaptation and mitigation approaches.
These results and uncertainties quantified from erratic regional climate response patterns and [CH4] dynamics collectively impact and govern decision-making processes. Moreover, these wide-ranging outcomes make it challenging to predict how regions will respond and adapt to driving factors and covariate departures, which inherently lead policymakers to approach geoengineering decision-making with caution. Another challenge regarding geoengineering deployment is the uncertainty representing each strategy’s efficacy and uneven regional impacts, challenges that often result in hesitancy regarding policy timing, risk assessment, and deployment. Unpredictability and unwavering uncertainties characterize intervention strategies motivate policymakers to favor established alternative mitigation strategies until these uncertainties and confidence intervals are better constrained. Moreover, the risk of catastrophic rebound from intervention highlights the temporal dependencies illustrated by geoengineering approaches; more concretely, if intervention ceases abruptly, global mean Tsurf may rise sharply and catalyze climatic shocks. Consequently, policymakers are inclined to mandate parallelized mitigation approaches to reduce the reliance on interventions and dependencies while suppressing potential climatic shocks with sustained sequestration efforts.
The potential benefits of geoengineering must be weighed against its associated risks and uncertainties through cost-benefit analyses, i.e., SAI strategies show promise for global cooling but risk destabilizing precipitation patterns and disproportionately affecting agriculture-dependent economies. Cooling effects exhibit significant uncertainty; e.g., the G1 experiment predicts Tsurf reductions of -6.87 °C to 4.38 °C with uneven regional distributions. G6Sulfur results demonstrate a global P increase of 0.03 mm day-1, contrasted with a -1.20 mm day-1 reduction in East Africa, posing significant regional drought risks. Temporal dynamics further complicate these analyses, i.e., solar dimming approaches, i.e., the G6Solar sub-experiment, provide cooling effects lasting 80–120 years. However, this comes at the cost of long-term dependencies on continuous intervention, which policymakers find unsustainable in general. Risk-reward balancing is another mechanism to guide climate policy; in particular, the 1pctCO2-cdr sub-experiment explores arctic amplification and reveals Arctic warming up to 13.08 °C, far exceeding global mean changes, thus accelerating permafrost thaw and methane release. This unabated warming in the Arctic and the activation of multiple feedback loops emphasize the need for Arctic-specific geoengineering trials, such as localized aerosol injections, to mitigate these nonlinear runaway effects. In addition, intervention escalation risks are identified, and an intervention escalation risk framework is adopted, e.g., the abrupt cessation of SAI may result in warming rates of up to 1.5 °C per decade, with catastrophic ecological consequences. Dual strategies combining emissions reduction and geoengineering could mitigate reliance on high-risk interventions.
Uncertainty requires iterative enhancement and application of sensitivity analyses and model architecture refinements to update, bolster, and improve model performance while reducing uncertainty and increasing confidence in geoengineering proposals (e.g., Tsurf variability of ± 2.55 °C in G6Solar simulations underscores the need for region-specific models). Policymakers increasingly rely on such analyses to guide incremental deployment or pilot testing. Phased approaches to geoengineering allow policymakers to target regions where benefits outweigh risks while implementing pilot testing, small-scale trials, and conditional deployment for risk mitigation. Policies may also link deployment to predefined thresholds, such as global temperatures exceeding 2.0 °C. Quantitative examples from model simulations highlight the guiding policies that prioritize mitigation and adaptation, invest in model refinement, integrated assessment model development, and state-of-the-art research, bolster multilateral governance frameworks, manage shared risks (i.e., delicate trade-offs between potential benefits and risks), and ensure equitable outcomes. By addressing these uncertainties, policymakers are better positioned to address scientific, ethical, and geopolitical challenges. Examples of quantitative insight derived from this study are iterated below to demonstrate policy responses to climatological trends: P variability necessitates regional pilot studies with controlled P monitoring, i.e., G6Sulfur simulations indicate global P anomalies of ± 2.5 mm day-1 with regional drought risk probabilities; delay SAI deployment until [CH4] dynamics are more universally and comprehensively understood, i.e., [CH4] magnitude ~ 6.38 ppb exacerbates warming in defined regions; adaptation and mitigation mandate serve as a backup to avoid intervention overreliance, with abrupt SAI cessation leading to a 1.5 °C decade-1 warming rate.
The contributions and sources of error resulting in simulated over- and underestimations may originate from computational methods, statistical limitations (e.g., climatological mean, temporal windowing), lack of variant ensemble diversity, spin-up perturbations, generalized parameterization, and new data assimilation techniques that impose new conditions on the system with restrictions and potential information loss from scaling efforts. To reduce possible errors and uncertainties originating from coarsely resolved scaling practices, ongoing efforts will prioritize stability and consistency by incorporating a multi-model multi-variant ensemble (i.e., UKESM1-0-LL, CanESM5, CNRM-ESM2-1, MIROC-ES2L, MPI-ESM1-2-LR) while introducing cutting-edge methodologies to reduce uncertainties and bolster validation efforts prior to 1950 – including artificial intelligence (AI), machine learning (ML), and AI optimization (AIO) frameworks – to better quantify and understand dynamics, patterns, and trends that emerge over time. These methodologies will enable knowledge discovery and confidence for recommendation systems and contingency formulation while offering the potential to optimize geoengineering designs by simulating nonlinear dynamics, analyzing large datasets with various data harmonization and assimilation frameworks, and minimizing risks with a multimodal neural network architecture.
An AIO implementation plan is defined by algorithm selection, data integration, and validation criteria to enhance the utility and rigor of these tools and methodologies. Suitable algorithms for geoengineering AIO tasks include reinforcement learning for SAI optimization, convolutional long short-term memory recurrent neural network (ConvLSTM) for spatiotemporal feedback prediction, variational autoencoders for reducing the dimensionality of massive climate datasets and process-based modeling outputs, and Bayesian optimization for uncertainty quantification during model predictions. For demonstration purposes, the data preprocessing workflow consists of training and testing the ConvLSTM network with historical SST, P, and atmospheric pressure data assimilated from MODIS observations, high-resolution ERA5 reanalysis data, and CMIP6 multi-model ensemble outputs. After training, validation protocol (e.g., cross-validation with independent climate data records), cost functions (RMSE, MAE), and skill scores are implemented to correct simulation biases or assess the fidelity, resilience, and reliability of regional climate feedback predictions generated from upsampled geoengineering intervention scenarios. Baseline quantities and these validation metrics not only assist with defining benchmarks for model agreement and bias detection, i.e., simulated v. observed intercomparisons but also improve existing and future climate feedback representations in model architecture while enhancing G6Solar and G6Sulfur simulations.
AI methods serve a critical role in optimizing geoengineering strategies since they can rapidly analyze massive numbers of scenarios to help identify the strategies that minimize risks and unintended consequences. However, AI introduces its own uncertainties, such as biases in feature selection and interpretability challenges, necessitating a balanced approach to integrating AI into decision-making processes. Additionally, the integration of AI into geoengineering playbooks presents inherent risks that are not yet fully understood35. These unknown risks and impacts may originate from a flawed optimization question, deploying imperfect algorithms in haste, or applying imperfect toolkits to an incomplete feature space with a flawed understanding of the Earth system. In light of persistent knowledge gaps, a dearth of observations, and industry standardized uncertainty quantification methodologies falling short, AIO strategies help address these areas of improvement while enhancing model performance. AI provides a powerful optimization tool, and the decision to implement a particular AIO algorithm is intrinsically an optimization question void of equifinality that concerns quality, performance, cost, and tractability50. We advocate for continued research into the efficacy, limitations, and implications of various climate intervention methods, emphasizing the need for a more comprehensive understanding of climate dynamics and calling for more refined models and international collaboration to mitigate the exacerbation of existing climate risks and strategically manage the potential irreversible impacts of global climate change.