Assessing Earth System Responses to Climate Mitigation and Intervention with Scenario-Based Simulations and Data-Driven Insight

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However, geoengineering faces a conundrum: accurately forecasting the consequences of climate intervention in a system for which we have incomplete observations and an imperfect understanding. We evaluate the potential implications of mitigation and intervention strategies with a set of experiments utilizing historical reanalysis data and scenario-based model simulations to examine the global response to deploying these strategies. Key findings included a global mean surface temperature and total precipitation increases of 1.374 \(\pm\) 0.481 \(^\circ\) C and 0.045 \(\pm\) 0.567 mm day −1 respectively over the observed period (i.e., 1950–2022). Mitigation and intervention simulations reveal pronounced regional anomalies in surface temperature and erratic interannual variability in total precipitation, with surface temperatures up to 7.626 \(^\circ\) C in Greenland, Northern Siberia, and the Horn of Africa down to -2.378ºC in Central Africa and Eastern Brazil, and total precipitation increases of 1.170 mm day −1 in Southern Alaska down to -1.195 mm day − 1 in Colombia and East Africa. Furthermore, [CH 4 ] dynamics indicated the potential to alter global and regional climate metrics but presented significant regional and global variability based on scenario deployment. Collectively, intervention and mitigation simulations tended to overestimate the variability and magnitude of surface temperature and total precipitation, with substantial regional deviations and scenario-dependent estimation heterogeneity for [CH 4 ]. Furthermore, forward projections indicate that both mitigation and intervention scenarios can lead to varied climate responses, emphasizing the complexity and uncertainty in predicting exact outcomes of different geoengineering strategies. By constraining our investigation scope to include monthly surface temperature, total precipitation, and atmospheric methane concentration [CH 4 ], we find these simulations were capable of accurately capturing departures but unable to perfectly represent patterns of warming and precipitation teleconnections clearly identified in the observational record. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Natural hazards Physical sciences/Mathematics and computing Physical sciences/Energy science and technology geoengineering climate change carbon cycle CMIP6 Earth Observations Earth system model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction State-of-the-art climate models failed to explain why global temperatures spiked in 2023, with a 0.2°C divergence between expected and observed annual mean temperatures, suggesting that the world may be entering uncharted territory 1 . The climate crisis confronts us with an urgent challenge that requires decision-making under conditions of high uncertainty and incomplete information. Climate change is accelerating while anthropogenic greenhouse gas (GHG) emissions continue to rise, risking the ability of the global community to achieve key climate stabilization targets 2 . For decades, climate models warned of linear atmospheric warming and nonlinear changes to the land surface. Despite this warning, global net emissions of anthropogenic greenhouse gases reached record levels over the last decade 3 . Current projections suggest that, barring major mitigation efforts, the cumulative concentration of greenhouse gases is on course to eclipse the 1.5°C climate stabilization threshold by 2035, a stark acceleration from previous forecasts 4 . This anticipated exceedance of the 1.5°C threshold is expected to cross multiple climate system tipping points 5,6 . These tipping points have the potential to activate runaway feedbacks driving ecosystem transformation and disrupting global interconnections, with impacts forecasted to affect Circumpolar regions, tropical rainforests, and global atmospheric circulation and hydrologic systems. 1 . If left unabated, there is a 67% likelihood that current emissions trends could exhaust the total remaining carbon budget of the planet in less than a decade 7 . These events drive an increasing demand for global solutions to mitigate climate change. Geoengineering - the deliberate large-scale intervention in the Earth system to manipulate specific biogeochemical processes or elements of the physical climate to counteract the impacts of climate change 7,8,9 - presents an intricate problem: what strategies to implement and when to implement them. As the planet continues to warm and GHG emissions reduction and carbon removal methods fall short of their goals, more direct modification may be required to offset growing climate impacts. The deployment of geoengineering technologies appears increasingly inevitable 10 . The goal of recent geoengineering proposals is to reduce global temperatures and carbon dioxide ([CO 2 ]) concentrations to pre-industrial levels 11,12,13,14 . Some methods involve offsetting climate change impacts by modifying the magnitude, rate, and extent of solar radiation in the form of solar radiation management (SRM) 15 . Other strategies may bolster the natural sequestration of CO 2 by enhancing carbon capture and sequestration through rock weathering 16 , afforestation, carbon aerosol capture, biochar, and ocean fertilization 16 . Alternatively, market-based instruments include carbon offset incentivization and global carbon accounting approaches (e.g., renewable energy credits). SRM strategies involve the injection of sulfate aerosols into the stratosphere to mitigate climate change and achieve regional climate balance by reflecting sunlight to cool the planet 18 . Aside from the feedback repercussions associated with this approach (i.e., amplified ocean acidification) 19 , large uncertainties propagate from this intervention strategy 20 . These uncertainties originate from the assumption of a uniform distribution of aerosols in both hemispheres; in reality, adopting SRM methods could precipitate an uneven spatiotemporal distribution of cooling effects across different latitudes and seasons with unknown secondary and tertiary consequences 10,11 . Stratospheric aerosol injection (SAI) remains a potential climate intervention mechanism to reduce surface temperatures 21,22,23,24 ; however, SAI deployment may result in catastrophic side effects 25 including reduced productivity or global breadbasket collapse 25,26,27 . In addition to aerosol injection, SRM proposals include deploying space reflectors and a space-based solar shield 28 , thinning cirrus clouds 29 , brightening marine clouds 30 , and more recently proposed, injecting ice particles 17 km above the earth surface to facilitate stratospheric dehydration 31 . According to the World Meteorological Organization (WMO), weather modification to mitigate climate warming are in operation today, with approximately 50 countries and nine US states deploying regional climate engineering projects without regulation via interstate provisioning, e.g., cloud seeding (i.e., rain enhancement) 32 . Cloud seeding operations are gaining momentum, which began nearly 65 years ago in China to induce rainfall after extended periods of heatwaves and drought 33 . Cirrus cloud seeding aids in precipitation formation by injecting silver iodide into the lower atmosphere to mimic ice crystallinity while modifying the physical chemistry of super-cooled mixed phase clouds, is gaining momentum 34,35,36 . Modeling studies integrating global circulation models (e.g., ECHAM6-HAM, CESM-CAM5) and scenario-specific forcing data explore the feasibility of these cloud seeding strategies and the associated impacts on radiative and climate response patterns. In particular, results from cirrus cloud seeding simulations demonstrate a reduction in cirrus-induced radiative effect (-0.8 to -1.8Wm -2 ), a reduction in CO 2 -induced climate change (50–85%), and mixed effects and uncertainty for extreme precipitation events 37 . Geoengineering techniques often involve searching physical parameters beyond the current observational record, thus overlooking data-driven forecasts (e.g., higher temperatures, stronger convection, increased aerosol load). Data-driven approaches are interpolative methods that require training datasets that span relevant spatial, temporal, and physical parameter ranges occupied by the input and prediction sets. Despite their promise, data-driven approaches to speculative scenarios may introduce additional risks and biases that emerge from fundamental assumptions as well as disjointed standards of practice. Accordingly, the uninformed application of data-driven methods can manifest into unintended risks and outcomes difficult to detect or diagnose even under manual inspection by domain experts 38,39 . Such oversight may originate from the failure to rigorously assess model performance with sufficient data while supporting interpretability, explainability, and transparency 40 . How do we harness data and state-of-the-art tools to address the fundamental challenges associated with geoengineering? What is the most optimal use of these techniques with intervention strategies given an incomplete observational record in addition to an imperfect knowledge and understanding of the earth system and associated feedbacks 41 ? What are the risks and unintended outcomes from these decisions? These solutions are not trivial. Given the evolving complexities and multidimensional dynamics of the Earth system, it is crucial to employ multimodal data and modeling frameworks that match the challenge in complexity and nuance. Utilizing existing observational systems and nascent technology will allow countries to monitor, interpret, and better understand the biogeochemical processes, synergistic relationships, and spatiotemporal variability governing the Earth system. We propose leveraging reanalysis products and model simulations may help optimize mitigation logistics, bridge multiscale intervention challenges, summarize contingency scenarios, and support continuous monitoring infrastructure. In this study, we examine various mitigation and geoengineering strategies and the subsequent impacts on the earth system by using historical estimates from the fifth-generation land-specific global atmospheric reanalysis data subset (i.e., ERA5 42 ) and a series of mitigation and intervention experiments supporting the Coupled Model Intercomparison Project, Phase 6 (i.e., CMIP6). Mitigation experiments are derived from the Carbon Dioxide Removal Model Intercomparison Project (i.e., CDRMIP 43 ) while geoengineering intervention experiment simulations are derived from the Geoengineering Model Intercomparison Project (i.e., GeoMIP 44,45,46 ). By utilizing these resources effectively, we can observe how global and regional climate baselines are changing and how the Earth and land-atmospheric responses will respond to mitigation and intervention deployment 47 . Results ERA5 Reanalysis For retrospective analysis, we examined high-resolution 2-meter temperature (t2m) and total precipitation (tp) surface variables derived from the ECMWF ERA5 Monthly-Averaged Climate Reanalysis dataset over a 73-year period (i.e., 1950–2022). This data product provides global physics-based data-driven land surface post-processed monthly-resampled observations on a 31-km rectilinear latitude-longitude grid (i.e., Methods ). The results below illustrate mean departures computed from monthly weighted climatologies with temporal thresholding (Fig. 1 ). Results indicate that climatology varied substantially from the observed record over this 73-year period, with Northern Siberia and the Horn of Africa experiencing an increase in surface temperature by as much as 7.626 \(^\circ\) C between 1950 and 2022 and surface temperature reductions in Central Africa, Southern Australia, Eastern Brazil, and Northwestern Mexico by -2.378 \(^\circ\) C; however, global mean surface temperatures steadily increased by 1.374 \(\pm\) 0.481 \(^\circ\) C. Moreover, regional surface temperature anomalies during this period ranged from − 1.332 \(^\circ\) C across the Canadian Boreal and Northern Siberia (e.g., Yamalo-Nenets Autonomous Okrug) to 1.192 \(^\circ\) C across the Arctic (e.g., Greenland, Northwestern Alaska, Chukotka Autonomous Okrug), Antarctica, South America (e.g., Uruguay), Africa (e.g., Angola), and Southeastern China. Additionally, global mean surface temperature departures accounted for a significant reduction of -0.297 \(\pm\) 0.331 \(^\circ\) C. When resampled, these global and regional patterns remained intact; however, the statistical ranges varied; specifically, regional surface temperature mean variability (i.e., departures) ranged between − 2.334-4.533 \(^\circ\) C, with increasing global mean surface temperature variability, i.e., 0.177 \(\pm\) 0.590 \(^\circ\) C. Precipitation patterns from 1950 to 2022 indicate increased total precipitation by 1.170 mm day -1 in Southern Alaska (e.g., Chugach and Copper River), the northern coast of Norway, South America (e.g., Western Ecuador, Northern Brazil), Southern India, Northwestern Indonesia, and Africa (e.g., Central Africa Republic, Democratic Republic of the Congo, Angola, South Africa), total precipitation reduction in South America (e.g., Colombia, Bolivia) and East Africa (e.g., Sudan, Ethiopia) by -1.195 mm day -1 , and a marginal increase in global mean total precipitation of 0.045 \(\pm\) 0.567 mm day -1 . Furthermore, regional precipitation departures indicate a reduction of -0.393 mm day -1 across the Amazon, Sahel, and Southeast Asia, while increasing total precipitation anomalies were quantified to nearly 0.125 mm day -1 in Southeastern Africa (e.g., Zimbabwe, Mozambique). Reductions in global mean total precipitation anomalies were computed, i.e., -0.029 \(\pm\) 0.054 mm day -1 . Similar to surface temperature variability after resampling indicated above, regional precipitation departures ranged from − 5.109–5.546 mm day -1 , with global mean precipitation departures of 0.045 \(\pm\) 0.567 mm day -1 (i.e., increasing precipitation, P > ET). CMIP6 | CDRMIP Simulations For prognostication and simulation purposes, two CO 2 removal (i.e., CDR) mitigation experiments were simulated with the UKESM0-1-LL model including ( 1a ) after abrupt quadrupling of CO 2 , instantiate one percent CO 2 reduction per year, i.e., 1pctCO2-cdr and ( 1b ) continuation of zero emission simulation branch from 1pctCO2-cdr after 1000PgC cumulative emissions threshold achieved, i.e., esm-1pct-brch-1000PgC . We employed additional historical and future controls of emissions reduction into the model simulations for retrospective and prognostic purposes. Concluding simulation analyses, intercomparisons are facilitated to identify discrepancies between these mitigation and intervention simulations relative to the resampled ERA5 observational record. 1a. 1pctCO2-cdr During this idealized experiment, CO 2 removal methods to reduce 4xCO 2 baseline levels by one percent per year until preindustrial control (i.e., PiC) is obtained and maintained, with increases in global weighted mean variability (i.e., detrending via annual climatology) of surface temperature (3.885 \(\pm\) 2.624 \(^\circ\) C), total precipitation (0.174 \(\pm\) 0.781 mm day -1 ), and [CH 4 ] (0.013 \(\pm\) 0.269 ppb) over the 1990–2149 time period (Fig. 2 ). Interestingly, the global weighted mean variability further condenses these trends to decreasing surface temperature and total precipitation (-1.909 \(\pm\) 1.216 \(^\circ\) C, -0.263 \(\pm\) 1.125 mm day -1 ), while [CH 4 ] increases (0.031 \(\pm\) 0.015 ppb). The most compelling relationship with this first experiment is the clear indication of pronounced Arctic amplification and widespread warming (-0.245-13.078 \(^\circ\) C), with hotspots near Hudson Bay, the Bering Sea, Baffin Bay, Eastern Europe, and the Middle East; in addition, amplified [CH 4 ] variability occurred along the Antarctic Peninsula coastline (-2.545-2.871 ppb), with pronounced warming surrounding Ellsworth Land, the Ross Ice Shelf, and north of Queen Maud Land. In contrast, large regions of the South Pacific Ocean and Indian Ocean demonstrated clear indications of cooling, potentially driven by the deep-water formation, westerlies, and surface currents, i.e., west wind drift from the Antarctic Circumpolar Current and the Peru Current and possibly catalyzed by the Northern Subpolar Gyre collapse as indicated by the quiescence of precipitation variability across the mid-latitudes. The most variability in total precipitation may be attributed to ocean-atmospheric equatorial interactions in the Arabian Sea (-12.228-9.064 mm day -1 ), Marshall Islands, and Eastern Bolivia. To differentiate regional patterns over this period, we examined the historical relationships between simulation outputs of surface temperature and total precipitation derived from this 1pctCO2-cdr experiment in alignment with the temporal windowing characterizing the ERA5 reanalysis observational record (i.e., 1990–2022, S5 ). Most significantly, these tri-decadal simulations predominately overestimate global weighted anomalies (6.878 \(\pm\) 0.007ºC) and marginally underestimate global weighted total precipitation (-0.102 \(\pm\) 0.003 mm day -1 ), with subsequent weighted mean departures for surface temperature and total precipitation (3.784 \(\pm\) 2.004 \(^\circ\) C, 0.431 \(\pm\) 0.906 mm day -1 ). Furthermore, surface temperature and total precipitation decreases (-0.010 \(\pm\) 0.365ºC, -0.024 \(\pm\) 0.892 mm day -1 ) while [CH 4 ] increases (0.018 \(\pm\) 0.251 ppb) over this period. Moreover, we examined global weighted departures, noting surface temperature and [CH 4 ] increases (0.013 \(\pm\) 0.723ºC, 0.033 \(\pm\) 0.234 ppb) while total precipitation decreases (-0.089 \(\pm\) 0.391 mm day -1 ) over this 33-year window. 1b. esm-1pct-brch-1000PgC For the next experiment, a zero emissions scenario is emulated from the previous 1pctCO2-cdr experiment, with [CO 2 ] removal continuing beyond achieving the 1000Pg threshold for cumulative emissions from 1950–2149. Over the course of 200 years of continued [CO 2 ] reduction, the global mean climatologies characterizing all three covariates increased , with surface temperatures rising 0.061 \(\pm\) 0.347ºC, total precipitation increasing by 0.003 \(\pm\) 0.331 mm day -1 and elevating [CH 4 ] by 0.001 \(\pm\) 0.169 ppb (Fig. 3 ). Alternatively, the global weighted mean variability exhibited by all three covariates decreased , with surface temperature departures ranging from − 5.446-4.854 \(^\circ\) C, total precipitation variability from − 5.404–5.218 mm day -1 , and [CH 4 ] departures ranging from − 1.729–2.519 ppb. This coupled relationship is further illustrated by the mean of global weighted mean variability computed for each covariate: -0.170 \(\pm\) 0.830 \(^\circ\) C, -0.011 \(\pm\) 0.665 mm day -1 , and − 0.001 \(\pm\) 0.188 ppb, respectively. We evaluated regional changes in mean covariability over time. Results suggest warming is less pronounced and more stochastic across the globe, with increasing surface temperature variability indicated west of the Antarctic Peninsula and distributed across the Norwegian and Greenland Sea, extending into the Arctic Ocean (4.854 \(^\circ\) C). Increasing precipitation departures and cluster densities were localized to equatorial regions of Eastern Brazil, French Polynesia, Arabian Sea and Bay of Bengal, Northern Vietnam, and east of the Coral Sea (7.471 mm day -1 ). In addition, increased variability of [CH 4 ] mean was observed in the Komi Republic, isolated anomalies near the Northwestern Passages, and relatively minor plume developments near the Amundsen Sea coast and between Wilkes and Victoria Land (2.519 ppb). In contrast, regional mean variability of surface temperature decreased near the Kara and Laptev Sea, Canadian boreal, and Amery Ice Shelf in Antarctica (-5.446 \(^\circ\) C) while [CH 4 ] departures decreased near Bellingshausen Sea and off the coast of Eastern Antarctica (-1.729 ppb). Bands of total precipitation variability are distributed across equatorial Oceania near the Hawaiian Islands, off the coast of Eastern Brazil (i.e., Pernambuco), and north of the Solomon Islands and Papua New Guinea (-7.383 mm day -1 ). Historical simulations from the esm-1pct-brch-1000PgC experiment were compared with the ERA5 observational record, ensuring temporal alignment over a 73-year period (i.e., 1950–2022) wherein zero emissions were emulated from the 1pctCO2-cdr run after achieving the cumulative emissions 1000Pg threshold ( S3 ). During the intercomparison, simulations overestimate global weighted mean of surface temperature (2.883 \(\pm\) 0.070ºC) and underestimate total precipitation (-0.101 \(\pm\) 0.003 mm day -1 ). In addition, global climatologies and weighted mean variabilities were computed, resulting in the following climatologies: surface temperature (-0.032 \(\pm\) 0.299 \(^\circ\) C, with ranges between − 3.197-3.444 \(^\circ\) C), total precipitation (0.001 \(\pm\) 0.330 mm day -1 , ranging from − 3.618–2.374 mm day -1 ), and [CH 4 ] (0.007 \(\pm\) 0.155 ppb, with ranges − 1.631–1.808 ppb). Thereafter, global weighted mean departures were computed and indicate simulations overestimated surface temperature (0.051 \(\pm\) 0.610ºC) and [CH 4 ] (0.010 \(\pm\) 0.172 ppb) while narrowly underestimating total precipitation (-0.0003 \(\pm\) 0.583 mm day -1 ). CMIP6 | GeoMIP Simulations Similarly, model simulations were conducted by the UKESM project with associated earth system model components (e.g., aerosol, atmospheric and oceanic general circulation, and biogeochemical models) across four geoengineering experiments for both retrospection and prognostication . These four experiments and their corresponding climatological and departure variability – temperature, precipitation, and [CH 4 ] anomalies differing from the long-term global climatological mean. These experiments include ( 2a ) an abrupt quadrupling of CO 2 while simultaneously integrating solar irradiance reduction, i.e., solar dimming (i.e., G1 ), ( 2b ) high-to-medium solar net forcing reduction from SSP585 to SSP245 (i.e., G6Solar ), ( 2c ) stratospheric sulfate aerosol injection to reduce net radiative forcing from SSP585 to SSP245 (i.e., G6Sulfur ), and ( 2d ) cirrus cloud seeding to reduce net radiative forcing from SSP585 by 1 Wm -2 (i.e., G7Cirrus ). 2a. G1 To establish a historical baseline, the first intervention experiment simulated the instantaneous quadrupling of atmospheric CO 2 concentration while simultaneously applying solar irradiance reduction (i.e., dimming) strategies. The results illustrate global surface temperature, total precipitation, and [CH 4 ] weighted mean variability from 1850–1949, ranging between − 6.873-4.377 \(^\circ\) C, -5.795-8.144 mm day -1 , and − 0.004-2.200 ppb respectively (Fig. 4 ). Moreover, the global climatological mean of surface temperature, total precipitation, and [CH 4 ] remained relatively stable, with surface temperature and total precipitation decreasing from − 0.010 \(\pm\) 0.472 \(^\circ\) C and − 0.010 \(\pm\) 0.321 mm day -1 while [CH 4 ] increased over the 100-year period, i.e., 0.003 \(\pm\) 0.180 ppb. This homeostatic behavior demonstrates a relatively balanced net radiation budget in response to reducing the solar constant by offsetting longwave radiative impacts with fast radiative responses and reducing shortwave radiation via stratospheric adjustments. From a regional context, global surface temperature mean variability increased from 1850–1949 (0.100 \(\pm\) 0.847 \(^\circ\) C), with increased surface temperature departures displayed in the Middle East, Northern Australia (i.e., Queensland), Shandong, Sendai Bay, and near several Antarctic ice shelves including the Ronne Ice Shelf, Ross Ice Shelf, and Amery Ice Shelf, all demonstrating increased warming trends and pronounced ‘hotspot’ activity and covariability (4.377 \(^\circ\) C). However, significant reductions in surface temperature variability were indicated primarily across the Chukchi and Beaufort Sea, as well as ‘mirroring’ the Norwegian Atlantic Current period of the Atlantic Meridional Overturning Circulation near divergence in the Iceland Basin (0.151 \(^\circ\) C). Over a period of 100 years, total precipitation variability increased less steadily with total incremental precipitation mean equivalent to 0.022 \(\pm\) 0.812 mm day -1 across regional surfaces. Examining the regional patterns of global precipitation mean departures, Micronesia, Uruguay, and Bangladesh experienced the highest magnitude relative to significant drying in Southeast Asia and eastern Brazil (-1.911-8.144 mm day -1 ). To inform baselines with carbon flux from G1 simulations, the historical evolution of global mean [CH 4 ] experienced abrupt pulses of increasing emissions yet maintained a marginal net sink progressively from 1850–1949, terminating with a net concentration difference of -0.001 \(\pm\) 0.206 ppb. High concentrations of atmospheric carbon aggregated across Scandinavia, Northwestern Siberia, and the coastline west of the Antarctic Peninsula near the Ross Ice Shelf and the Amundsen Sea (2.200 ppb), with noticeable opposing reductions in global atmospheric [CH 4 ] variability exhibited along the eastern coastline of Antarctic coastline, emanating northeasterly from the Amery Ice Shelf into the Indian Ocean (-1.911 ppb). Intercomparisons between resampled ERA5 reanalysis data and G1 simulations were not conducted due to the temporal misalignment, i.e., ERA5 (1950–2022) v. G1 (1850–1949). However, after detrending G1 climatology to compute global mean departures from 1850–1949, surface temperature and total precipitation departures decreased from 1950–2022 relative to 1850–1949 (-0.242 \(\pm\) 0.0.326 \(^\circ\) C, -0.166 \(\pm\) 0.768 mm day -1 ), with a range of -2.967-4.624 \(^\circ\) C and − 8.598–2.262 mm day -1 , respectively. 2b. G6Solar The second geoengineering experiment simulated high-to-medium solar forcing reduction efforts via solar irradiance curtailment (i.e., SSP585 to SSP245, 2020–2100). The radiative transfer dynamics evolve in response to instantiated variables, forcings, drivers, and parameterization in the model. In essence, this experiment illustrates how solar geoengineering facilitates cooling effects and the resulting maps and metrics demonstrate its utility and veracity for exploration and deployment. Global weighted climatological means for surface temperature, total precipitation, and [CH 4 ] increased marginally from 2020–2100 (Fig. 5 ), ranging from − 1.146–6.593ºC (1.275 \(\pm\) 1.262ºC), -2.622-5.256 mm day -1 (0.045 \(\pm\) 0.347 mm day -1 ), and − 4.612–7.120 ppb (1.575 \(\pm\) 1.075 ppb). Weighted mean variability of global surface temperature and total precipitation from 2020 to 2100 ranged between − 14.479-1.615 \(^\circ\) C, -10.523-3.915 mm day -1 , and − 11.216–0.643 ppb, with a reduction in global mean variability for surface temperature, total precipitation, and [CH 4 ] as indicated by -2.545 \(\pm\) 2.605 \(^\circ\) C, -0.083 \(\pm\) 0.612 mm day -1 , and − 4.368 \(\pm\) 0.623 ppb. According to model simulations from this experiment, a number of regions exhibit hotspots for elevated surface temperature and [CH 4 ] departures from planetary homeostasis, i.e., climatological norm. These hotspots include surface temperature anomalies in Northern and Southeastern Brazil, Northern coastlines of Nunavut, western coast of Baffin Bay, Eastern Europe, Northern Siberia, South Australia, Gujarat, and Indonesia. Aside from the clear equatorial gravitation for precipitation patterns, Uruguay exhibits periods of extended precipitation consistency. Alternatively, this intervention strategy introduces the concept of ‘cooling’ down the earth: in terms of ‘cold spots’ indicated by these results, some spatial patterns emerge. In particular, these departures illustrate how the climate is changing and moving away from climatological norms of the past. Though mostly constrained to open water in high latitudes, some of these cold spots include regions in the Bering Sea, Hudson Bay, Barents Sea, and extensions westward into the Greenland and Norwegian Sea. Abiotic simulations predominately underestimate global weighted mean variability of surface temperature (-0.165 \(\pm\) 0.595ºC), total precipitation (-0.008 \(\pm\) 0.653 mm day -1 ), and [CH 4 ] (-0.136 \(\pm\) 0.561 ppb). We examined the resulting global mean temporal differencing and covariability between historical observations from resampled ERA5 reanalysis data with G6Solar simulations (i.e., 2020–2022). During ERA5 and G6Solar model intercomparisons of surface temperature and total precipitation from 2020–2022, global weighted mean of mean differencing and standardized metrics were computed by spatially weighting the anomalies (i.e., weighted), removing climatology trends (i.e., unweighted), and applying a global mean operation across each observation and prediction originating from the reanalysis or simulation output ( S3 ). Thereafter, the simulations from 2020–2022 indicated overestimations of surface temperature on the order of 0.017 \(\pm\) 0.064ºC and underestimated total precipitation variability by -0.102 \(\pm\) 0.001 mm day -1 . Interestingly, from 2020–2022, this upward-trending pattern for global mean surface temperature and total precipitation variability was observed and analyzed with respect to overestimation magnitudes. Furthermore, precipitation experiences intrinsic dynamic shifts in regime 48 ; therefore, temporal subsetting isolated trends for validation and sensitivity analyses. From 2020–2021, global weighted mean variability differencing of surface temperature and total precipitation from G6Solar simulations and ERA5 were overestimated (1.981 \(\pm\) 0.031ºC, 0.855 \(\pm\) 0.004 mm day -1 ) while both covariates subsequently overestimated across the 2021–2022 period, i.e., differencing resulted in surface temperature and total precipitation mean variability of 2.030 \(\pm\) 0.080ºC and 0.881 \(\pm\) 0.030 mm day -1 , respectively, yielding net overestimations in surface temperature and total precipitation over this three-year validation window. 2c. G6Sulfur Stratospheric aerosol injection is simulated in this experiment (i.e., 2020–2100) to demonstrate the variability of forcing mechanisms and consequential impacts on the Earth system. Steady increases in global mean climatologies of surface temperature, total precipitation, and [CH 4 ] are represented by ranges of -1.051-6.409ºC (1.346 \(\pm\) 1.302ºC), -2.223-3.126 mm day -1 (0.033 \(\pm\) 0.327 mm day -1 ), and − 4.432–6.376 ppb (1.372 \(\pm\) 1.175 ppb) until the end of the century (Fig. 6 ). Interestingly, all three covariates collectively experience reductions in global weighted mean variabilities, i.e., departures between the bounds of 2020–2100 (-2.701 \(\pm\) 2.635ºC, -0.060 \(\pm\) 0.653 mm day -1 , and − 4.633 \(\pm\) 0.993 ppb), illustrating persistent reductions in global mean surface temperatures and atmospheric mean concentration, though erratic oscillations prompt marginal reductions in total precipitation. Additional marked increases in global weighted mean variability for total precipitation variability across the entirety of Eastern Brazil – from Bahia to São Paulo – as well as surface temperature and [CH 4 ] anomalies near the Ross and Amery Ice Shelf. Comparing ERA5 reanalysis data with historical simulations of SAI, a marked uptick in global mean temperatures is apparent prior to climatology detrending, i.e., increasing warming trend is more explicit, suggesting potential feedback interactions manifesting and/or contributing to amplified overestimations of water cycle covariates, notably during shoulder or transition seasons. Contemporary and CMIP6-derived simulation projections overestimate the magnitudes of some of these marginal/fringe oscillations, with future projections (i.e., 2023–2100) of high-to-medium reduction (i.e., SSP585 to SSP245) via SAI demonstrating an even greater propensity to overestimate these covariates with large margins of magnitude differences. Therefore, temporal alignment with the observational record from ERA5 reanalysis during 2020–2022 allows observation-driven data and simulation output intercomparison to identify any correlations, relationships, or behaviors that manifest and contribute to global change ( S3 ). Computing these metrics and global mean variabilities (i.e., departures) via climatology removal and spatial averaging, it is determined that model simulations exhibit wide variability in covariate ranges (-2.149-1.558ºC, -5.713-3.561 mm day -1 , -3.564-2.003 ppb); however, on average, these simulations underestimate global weighted mean variabilities of all covariates, i.e., surface temperature (-0.002 \(\pm\) 0.430ºC), total precipitation (-0.165 \(\pm\) 0.595 mm day -1 ), and [CH 4 ] (-0.162 \(\pm\) 0.165 ppb). Thereafter, the simulations from 2020–2022 indicated an overestimation of surface temperature (0.017 \(\pm\) 0.064ºC) and underestimations of total precipitation (-0.102 \(\pm\) 0.001 mm day -1 . From 2020–2022, this overall upward-trending pattern and potential overestimation of global mean surface temperature and total precipitation variability was analyzed with temporal shifts over the three-year period. From 2020–2021, global weighted mean variability differencing of surface temperature and total precipitation from G6Sulfur simulations and ERA5 was overestimated (1.981 \(\pm\) 0.031ºC, 0.855 \(\pm\) 0.004 mm day -1 ) while similarly, 2021–2022 simulations overestimated observations, i.e., differencing resulted in surface temperature and total precipitation mean variability of 2.030 \(\pm\) 0.080ºC and 0.881 \(\pm\) 0.030 mm day -1 , respectively, yielding net overestimations in surface temperature and total precipitation. 2d. G7Cirrus Evaluating the radiative forcing impacts of cloud seeding on land-atmospheric interactions foretells a different story. For this experiment, simulations promote increases in rate of cirrus ice crystallization (i.e., G7Cirrus ) to mitigate high emission baseline forcing from SSP585 by 1 Wm -2 , resulting in substantial increases in global weighted mean of surface temperature (2.028 \(\pm\) 1.567ºC; -0.216-7.716 \(^\circ\) C), total precipitation (0.093 \(\pm\) 0.412 mm day -1 ; -3.936-3.986 mm day -1 ), and [CH 4 ] (1.515 \(\pm\) 1.124 ppb; -4.515-7.573 ppb) from 2020–2100 (Fig. 7 ). Additionally, global mean departures indicated consistent reduced variability of surface temperature (-4.089 \(\pm\) 3.321ºC), total precipitation (-0.184 \(\pm\) 0.741 mm day -1 ), and [CH 4 ] (-4.422 \(\pm\) 0.780 ppb) under cloud seeding-induced radiative effects. These patterns are most notably illustrated across the Arctic, with increasing surface temperature via weighted mean differencing across Greenland and Baffin Island, Northern Scandinavia, British Columbia and the Canadian Rockies, Southern Kazakhstan and Mongolia, Northern Siberia (i.e., Northern Trans-Siberian extent from Yamalo-Nenets Autonomous Okrug to the Sakha Republic), and the foothills and rainforests east of the Andes Mountains (i.e., Northern Argentina, Paraguay, Bolivia, and Western Amazonas). Alternatively, a number of cool spots proliferate across the Arctic as well; namely, the Chukchi and Beaufort Sea, Hudson Bay, Svalbard, and parts of the Barents and Kara Sea. Total precipitation events appear localized to equatorial regions of the western Arabian Sea and the North Pacific Ocean, though increasing trends of total precipitation are illustrated along the western coast of Jalisco, Central Bahia in South America, Uruguay, Southern Malawi, and Brisbane. Alternatively, significant dry spells are depicted in the Indian Ocean south of the Bay of Bengal and west of North Sumatra, east of the Philippines (i.e., Guam) and southwestern Colombia. Strong [CH4] anomalies are displayed in the Nord-du-Québec region, along the coast of Terra Nova Bay and the Gerlache Inlet (Amery Ice Shelf), a substantial anomaly near the Ross Ice Shelf in the South Ocean, and finally, localized hot spots in western Kalimantan and North Sumatra. Notable cool spots emerge off the coast of Queen Maud Land (i.e., Utsteinen Nunatak, East Ongul Island). During ERA5 and G7Cirrus model intercomparisons of surface temperature and total precipitation from 2020–2022 - and after climatology was removed and spatial averaging was applied to compute global mean - the simulations overestimated the weighted mean of surface temperature (0.015 \(\pm\) 0.318ºC; -1.997-2.122ºC) and underestimated total precipitation (-0.004 \(\pm\) 0.321 mm day -1 ; -4.267-3.357 mm day -1 ). Surface temperature demonstrated interannual volatility between simulations and reanalysis data but generalized to a net negative − 0.116 \(^\circ\) C reduction from 2020–2022. Total precipitation revealed similar yet inverted patterns of volatility. Namely, these patterns illustrated divergent temperatures yielding periods of higher total precipitation accuracy - with a generalized diverging net positive trend of 0.052 mm day -1 (i.e., P > ET). Computing global weighted mean departures for these variables from 2020–2022 indicated the model’s tendency to underestimate both surface temperature (-0.611 \(\pm\) 0.023ºC; -4.172-4.717ºC) and total precipitation (-0.102 \(\pm\) 0.001 mm day -1 ; -4.771-5.453 mm day -1 ) based on temporal bounding conditions in the interest of ERA5 intercomparison efforts. The historical relationship (i.e., 2020–2022) among ERA5-derived and simulated surface temperature and total precipitation resulted in a global annual weighted mean differencing of 1.792 \(\pm\) 0.028ºC and 0.893 \(\pm\) 0.023 mm day -1 . Furthermore, we spatially flattened and temporally differenced the ‘validation’ datasets from 1990–2022, 1950–2022, and 2020–2022 to generate time series plots in Fig. 8 illustrating surface temperature and total precipitation variability over a 73-year time period. These plots illustrate how simulations drift from the observational record. Discussion In this study, we examined various mitigation and geoengineering experiments to understand the potential impacts of these strategies and to identify the most feasible solutions to address global climate change. Some of these experiments included CO 2 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 multi-scale biogeochemical processes. Moreover, potential unintended consequences resulting from each of these geoengineering scenarios are important to highlight; in particular, surface temperature and total precipitation briefly reach - and exceed - stabilization thresholds during the contemporary period in many of these experiments, with rapid warming and increased precipitation bolstering an enhanced runaway greenhouse effect. Collectively, intervention and mitigation simulations tended to overestimate the variability and magnitude of surface temperature and total precipitation, with substantial regional deviations and scenario-dependent estimation heterogeneity for [CH 4 ]. This highlights the challenges to accurately quantify and project greenhouse gas 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 exact outcomes of different geoengineering strategies. This suggests that model uncertainty may promulgate through the projections but represent a near-to-observational record that is nonetheless 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 apply 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. Retrospectively, global mean surface temperatures and total precipitation derived from ERA5 reanalysis data indicate steady increases of 1.374 \(\pm\) 0.481°C and 0.045 \(\pm\) 0.567 mm day -1 respectively. However, significant regional variations were identified, with Greenland, Northern Siberia, the Horn of Africa, and the Antarctic coastline experiencing elevated surface temperatures as high as 7.626°C, while other regions including Central Africa, Southern Australia, Eastern Brazil, and Northwestern Mexico observed notable reductions in surface temperature (-2.378 \(^\circ\) C). Similarly, regions including Southern Alaska (e.g., Chugach and Copper River), the northern coast of Norway, South America (e.g., Western Ecuador, Rio Grande do Sul, Uruguay), Southern India, Northwestern Indonesia, and Africa (e.g., Central Africa Republic, Democratic Republic of the Congo, Angola, South Africa) experienced increased total precipitation (1.170 mm day -1 ), while South America (e.g., Colombia, Bolivia, Amazonas) and East Africa (e.g., Sudan, Ethiopia) experienced reductions in total precipitation (-1.195 mm day -1 ). Though imperfect in comparison to observational records, these mitigation and intervention simulation experiments capture large-scale Earth system dynamics well; however, some discrepancies do emerge. Utilizing global mean surface temperature and total precipitation as points of departure – with the understanding that regional-to-localized dynamics still require more spatially explicit refinement in Earth System Model frameworks 49 - reanalysis and model intercomparisons act as a quality assessment and validation baseline to inform calibration updates (i.e., forcing observations, parameterization, drivers) and improve future simulations. 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. Mitigation scenarios were simulated to demonstrate the potential of CO 2 removal strategies to substantially alter global climatologies in the future; in particular, initialization of the 1a 1pctCO2-cdr experiment resulted in global mean climatologies demonstrating consistent increases in surface temperature, total precipitation, and [CH 4 ] from 1990–2149 (3.885 \(\pm\) 2.624 \(^\circ\) C, 0.174 \(\pm\) 0.781 mm day -1 , 0.013 \(\pm\) 0.269 ppb). In contrast, surface temperature and total precipitation were reduced while [CH 4 ] increased from 1990–2022 (-0.010 \(\pm\) 0.365ºC, -0.024 \(\pm\) 0.892 mm day -1 , 0.018 \(\pm\) 0.251 ppb), suggesting an effective cooling and drying mechanism in the short-term yet largely underestimates the evolution of surface temperature and total precipitation (4.161 \(\pm\) 0.054ºC, 0.980 \(\pm\) 0.010 mm day -1 ). Critically, this experiment precipitated pronounced Arctic amplification, with surface temperature in excess of 13.078°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 surface temperature, total precipitation, and [CH 4 ] from 1950–2149 in comparison to 1a i.e., 0.061 \(\pm\) 0.347ºC, 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 surface temperature reductions for the 1990–2022 simulation period, with marginal increases in total precipitation and [CH 4 ] trajectories (-0.032 \(\pm\) 0.299 \(^\circ\) C, 0.001 \(\pm\) 0.330 mm day -1 , 0.007 \(\pm\) 0.155 ppb). The 1b experiment indicates that even under aggressive CO 2 removal and carbon management strategies, complex climate feedbacks remain unfettered 50 . 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 surface temperature, total precipitation, and [CH 4 ] patterns with varying regional impacts. The 2a G1 experiment (i.e., 4xCO 2 and solar dimming) illustrates global radiative balance by reducing solar irradiance to stabilize climatological feedbacks, resulting in significant surface temperature modulations (i.e., slight cooling), extensive variability in total precipitation, and increasing [CH 4 ] across the simulated period, i.e., 1850–1949 (-0.010 \(\pm\) 0.472 \(^\circ\) C, -0.010 \(\pm\) 0.321 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 2b G6Solar experiment, high-to-medium solar forcing reduction promoted cooling trends, with elevated temperature and [CH 4 ] hotspots despite an overall reduction in global mean variability (i.e., departures). Despite reducing surface temperatures, total precipitation, and [CH 4 ] from 2020–2022 (-2.545 \(\pm\) 2.605 \(^\circ\) C, -0.083 \(\pm\) 0.612 mm day -1 , -4.368 \(\pm\) 0.623 ppb) – and cultivating cool, dry spots across the globe in the short-term – surface temperatures, total precipitation, and [CH 4 ] rebounded and increased over the 2020–2100 simulation period (1.275 \(\pm\) 1.262ºC, 0.045 \(\pm\) 0.347 mm day -1 , 1.575 \(\pm\) 1.075 ppb). The 2c 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 precipitation departures (i.e., variability) by doing so. Additionally, the results suggest this strategy may present abrupt climatic changes and pose significant long-term stability issues beyond initial beneficial impacts are achieved and aerosol effects are diminished. Similarly, the 2d experiment ( G7Cirrus ) facilitated an increase in surface temperature and [CH4] initially, though regional patterns of reduced temperature variability were observed. In the long term, the impacts on precipitation variability are significant. Collectively, these results suggest a complex interplay of oscillating dynamics between global, regional, and localized climate regimes and highlight the challenges to forecast accurate outcomes under mitigation and intervention mechanisms. We extended the distribution curve tails toward a ‘new normal’ by increasing CO 2 , seemingly magnifying contemporary observed changes of punctuated extreme weather and precipitation 51 . This variability introduces shifts in regional climates, though it may also be responsible for the increased warming trend across the globe. While the range of temperature and precipitation in 1b is less 1a , precipitation is increasingly variable in the models, with interannual shifts in precipitation 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 precipitation, governed by biogeochemical processes that are not fully captured by the models, is of concern. Moreover, by employing and extending the 1a experiment beyond the 1000PgC benchmark, this facilitates increasing loss of atmospheric carbon [CH 4 ] beyond net zero that may have biogeochemical consequences over time. Regional incongruencies in temperature and precipitation are localized to hot and cool dry ecotones exhibiting high net primary productivity and the persistent conversion from carbon sinks to sources. These 2b simulation outputs of global weighted mean variabilities indicate synergistic co-regulated feedbacks, with persistent trends characterized by abrupt pulses; specifically, [CH 4 ] began and ended the simulation period with net emissions in terms of annual magnitude, but throughout most of the simulation period, [CH 4 ] variability decreased and remained low. In contrast, total precipitation variability illustrated anticorrelated behavior, i.e., began and ended the simulation period with minimal precipitation events and accumulation but rather a steady increase followed by a gradual decrease – in enhanced total precipitation variability. Surface temperature characteristically stimulates climate feedback patterns governing water vapor distribution and precipitable accumulation. During this validation window, surface temperature and total precipitation differencing and dimensionality reduction via spatial flattening demonstrates that over regions of high variability of total precipitation, surface temperature departures were marginal, remaining stabilized (i.e., oceans, equatorial tropics); however, less precipitation variability prompted an elevation of surface temperature variability, triggering positive climate feedbacks (i.e., high latitudes). These trends remain periodic with gradual increases in surface temperature and total precipitation – in concert with net release yield from [CH 4 ] – through the end of the century, followed by a slight stabilization period. Marked reductions in global mean departures from 2c simulations suggest the employment of this strategy will indeed cool the planet while synergistically amplifying the positive coupling feedbacks between temperature and precipitation, 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 total precipitation variability across the entirety of Eastern Brazil – from Bahia to São Paulo - as well as surface temperature and [CH 4 ] 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 – 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, when examining these figures, it is important to note that global mean variability between the two experiments from 2020–2100 results in strikingly different outcomes, thus illustrating how climatologies change, anomalies evolve, and global climate change is fostered by land-ocean-atmospheric interactions over space and time. Surface temperature variability in 2c is likely coupled with unique surface flux exchanges and biogeophysical dynamics, though it is more likely coupled with the erratic variability of total precipitation. SAI 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 increases in temperature and precipitation for the first 30 years after initialization, followed by an abrupt jump in global mean temperature. These dynamics may be caused by feedback interactions with the Earth systems or due to a loss of atmospheric sulfate over time, but the abating of temperatures allowing for a stabilized system without fossil fuel reductions is of concern, when followed by abrupt increases. This strategy is less variable on the year-to-year scale but presents problems for long-term climate stability. Relative to baseline simulations, SAI methods facilitate similar trends to these patterns. However, the magnitude of global weighted mean variability of total precipitation is significantly higher as a consequence of 2b , while surface temperature exhibits lower variability. In addition, 2a and 2b produced anticorrelated covariate trends, with 2a showing higher variability of surface temperature, precipitation, and [CH 4 ]. This is due to solar irradiance reduction techniques. The 2a 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 very different approaches seek to address and resolve uncertainty challenges by altering light or matter. Another example of the anticorrelated covariate trending is 2c and 1a . In this case, 2c induces a reduction in surface temperature and atmospheric methane variability while increasing total precipitation variability on a global scale. In contrast, 1a increases surface temperature variability coupled with a less variable global weighted mean in total precipitation. The variability of global weighted mean of [CH 4 ] was significant during 2c , representing nearly four orders of variable magnitude relative to [CH 4 ] output variability from 1a . The dissimilar system responses reiterate that small perturbations can have significant effects when magnified across space and time. During the 2d experiment, the range of mean temperature is reduced considerably, together with surface temperature. The elevation in precipitation may be marginal but the variance has a long tail towards 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. Climate change remains a critical issue, and the likelihood of geoengineering intervention in the future is nearly certain. 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 not yet achieved the necessary deployment scale required for climate change mitigation, and their impacts on the Earth system remain poorly 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 a period of 73 years (i.e., 1950–2022) as well as six mitigation and intervention simulations from CMIP6 experiments (i.e., CDRMIP, GeoMIP) from 1850–2149, 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. The potential contributions and sources of error resulting in simulation over- and under-estimations may originate from computational methods, statistical liberties (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 to the system with restriction and potential information loss from scaling efforts. To reduce potential error and uncertainty originating from coarsely resolved scaling practices, future efforts will incorporate a multi-model multi-variant ensemble (i.e., UKESM1-0-LL, CanESM5, CNRM-ESM2-1, MIROC-ES2L, MPI-ESM1-2-LR). Though stability and consistency are important, the ability to validate historical simulations remains a challenge. To bolster validation efforts pre-1950, we will introduce cutting-edge methodologies, including artificial intelligence (AI) frameworks and optimization (AIO) to better quantify and understand dynamics, patterns, and trends that emerge over time. Furthermore, this adoption will enable knowledge discovery and confidence for recommendation systems and contingency formulation. AI methods seem poised to 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. 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 tractability 52 . The integration of AI into geoengineering playbooks presents inherent risks that are not yet fully understood 40 . 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. Coupled with the dearth of uncertainty quantification and transparency, AIO strategies help address knowledge gaps and improve model performance. 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 on global climate change. Methods We utilized ERA5 reanalysis data as well as mitigation (i.e., CDRMIP) and intervention (i.e., GeoMIP) simulations to first examine baseline conditions to gain a retrospective understanding of climate warming then prognosticating climate response patterns under a variety of mitigation and climate engineering scenarios. We compared observation-based ERA5 reanalysis data with simulation outputs from the CDRMIP and GeoMIP experiments. The temporal bounds of the simulation experiments were constrained to the observational period (i.e., 1950-2022) for surface temperature and total precipitation intercomparisons (i.e., 1pctCO2-cdr ), 1950-2022 (i.e., esm-1pct-brch-1000PgC ), and 2020-2022 (i.e., G6Solar ; G6Sulfur ; G7Cirrus ). The UKESM1-0-LL model (i.e., r1i1p1f2 ensemble member) was selected for this study because it is one of only six projects involved with both mitigation and climate engineering efforts (i.e., CDRMIP, GeoMIP) while offering a breadth of ensemble variants and surface variables at various temporal resolutions on a Native N96 grid. For sensitivity analyses, precipitation flux simulations derived from these simulations was converted to total precipitation (i.e., kg m -2 s -1 to mm day -1 ) based on temporal windowing and the global surface basin. In addition, the sub-tropospheric multilevel mean of [CH 4 ] was converted to parts per billion (i.e., mol mol -1 to ppb). ERA5 Reanalysis ERA5 Reanalysis observations (e.g., 2-m temperature, total precipitation) were regridded from (721, 1440) at 0.25 resolution with 3.88B samples to (143, 191) at 2.348 resolution with 16.03M observations (i.e., 27320 global observations per annum). The standard reference period as defined by the WMO identifies climate normal (i.e., 1991-2020); however, ERA5 Reanalysis data utilizes 1981-2010 as a base period, and therefore, we selected this time period, i.e., 1981-2010. ERA5 monthly averaged high-dimensional reanalysis datasets were extracted from the European Centre for Medium-Range Weather Forecasts Climate Data Store portal generated by Copernicus Climate Change Service. The CDS API tool was utilized to efficiently query the database with appropriate parameters in place, i.e., global coverage over a 73-year period. Following regridding and scaling, these datasets were loaded, concatenated and restructured into a dataframe. CMIP6 Simulations In addition, 699.72M modeling outputs including abiotic measurements, e.g., surface temperature, precipitation, and [CH 4 ] derived from CMIP6 projects including the Geoengineering Model Intercomparison Project (i.e., GeoMIP) and the Carbon Dioxide Removal Model Intercomparison Project (i.e., CDRMIP) experiments (i.e., concluding NaN dropping via bilinear interpolation, backfilling, and forward-filling, 341.40M and 358.32M, respectively). Subsets of these experiments were searched and downloaded from the LLNL metagrid node with the ESGF PyClient API. Numerous geoengineering and CO 2 removal intercomparison scenarios adopt sophisticated architectures and assessment protocols (e.g., CDRMIP, GeoMIP). However, mitigation efforts are limited in scope and often formulate uninformed strategies based on single high-emissions pathways inconsistent with near-term projections. The CDRMIP project aims to consolidate Earth system models within a unified framework to evaluate the feasibility, effects, and challenges associated with CDR technologies. The GeoMIP project aims to meet this research need by standardizing experiments across participating climate models. This initiative will enable identification of both commonalities and discrepancies of climate response to mitigation and intervention in model predictions, thereby contributing vital insights into potential outcomes of significant global efforts. We examined mitigation and intervention strategies employed by assessment protocol and SSP scenarios to identify global historical and future relationships and climate change indicators from 1850-2149 (e.g., surface temperature, precipitation, and [CH 4 ]). Due to escalating extreme events and more support growing for weather modification tactics, it was necessary to not only examine anomalies, trends, and bounds of the datasets but to also constrain the temporal look-forward period to 2050 in the interest of mitigation and policy implementation. We examined these experimental runs from 1850 to 2149, with careful scrutiny given to periods of data misalignment and natural disturbance events (e.g., Pinatubo), and further examination during the years 2020 and 2050, notating critical information that may prove useful in terms of deployment decision-making. A more comprehensive list of results distributed by experiment, period, covariate, and uncertainty is provided in S1 . GeoMIP simulations were analyzed to understand the historical and future implications of current strategies and the potential consequences of implementing geoengineering practices. Baselines were established to provide historical lenses (i.e., G1) and real-world atmospheric forcing (i.e., ERA5) to determine feasibility of contemporary geoengineering proposals and the associated impacts on the earth system over time. Concluding initial baseline runs, we extended the temporal retrospection period by 100 years with historical simulations (i.e., 1850-1949, G1 ) and the prognostication period by 77-128 years with forward projections of intervention (i.e., 2023-2100, G6Solar , G6Sulfur , G7Cirrus ) and mitigation experiments (i.e., 2023-2149, 1pctCO2-cdr , esm-1pct-brch-1000PgC ). Data Preprocessing These data products were loaded, appended, scaled, and reframed in each experiment-specific sequencing of the dataframe (i.e., CDRMIP: (199065600, 5) 1PCT ), GeoMIP: (80621568, 5) G1 , (159252480, 5) GSSC ) prior to assimilating with observation-derived ERA5 data with a bilinear periodic regridding algorithm (i.e., xESMF Regridder) to improve performance, compatibility, and interpretability. Because the CMIP6 experiments yielded daily outputs, monthly mean resampling was conducted across these simulation datasets to align with ERA-Land reanalysis monthly observations. ERA5 global monthly averaged observations (e.g., 2-meter temperature, total precipitation): 24.25M regridded data points from 1950-2022 CMIP6 CDRMIP and GeoMIP Experiments (e.g., surface temperature, precipitation flux, [CH 4 ]): 16.03M data points from 1850-2149 1pctCO2-cdr : In wake of 4xCO 2 preindustrial baseline, one percent CO 2 reduction per year is prescribed until preindustrial control is reached and maintained (1990-2149) esm-1pct-brch-1000PgC : After 1000Pg cumulative emissions threshold achieved, zero emissions are simulated after 1pctCO2-cdr run (1950-2149) G1 : 4xCO 2 mitigation via solar radiation management (1850-1949) G6Solar : High forcing scenario reduction to medium forcing via solar radiation management, i.e., solar irradiance reduction (2020-2100) G6Sulfur : High-to-medium forcing scenario reduction; sulfate aerosol injection, SAI (2020-2100) G7Cirrus : High forcing scenario baseline mitigation via increases in rate and magnitude of cirrus ice crystallization, i.e., cloud seeding (2020-2100) These dataframes were concatenated along the temporal dimension, resulting in a (23951989, 93) dataframe with 93 variables obtained from ERA5 reanalysis observations across 1950-2022 (i.e., 2-meter temperature, total precipitation), CDRMIP 1pctCO2-cdr and esm-1pct-brch-1000PgC experiments from 1950-2022 and 1990-2022 respectively (i.e., surface temperature, precipitation flux, [CH 4 ]), GeoMIP G1 spin-up simulations from 1850-1949 (i.e., surface temperature, precipitation flux, [CH 4 ]), and GeoMIP G6 and G7 geoengineering experiments from 1850-2100, and radiatively forced OSCAR v3.3 modeling outputs across 1750-2022. Beyond 2022, G6 and G7 experiments (2023-2149) and OSCAR v3.3 modeling output (2023-2100) serve as proxy for testing the prediction abilities of the model (i.e., seasonal, annual, decadal, semicentennial) while accounting for model drift anomalies that could propagate in space and time. In addition, employing this data for testing helps identify key temporal relationships that may provide more comprehensive understanding of local-to-regional effects of disaggregation and down-sampling methods to further disentangle anomaly detection (i.e., localizing trend and/or feedback dynamics that require data-informed scenario-based decision-making. Various scenarios in place. Declarations Acknowledgements The ideas and views conceived and conveyed within this article are the authors’ own and do not represent the opinion or policy of the National Aeronautics and Space Administration, Jet Propulsion Laboratory, or California Institute of Technology. BG’s participation was supported by an appointment to the NASA Postdoctoral Program at the Jet Propulsion Laboratory, California Institute of Technology, administered by Oak Ridge Associated Universities. NASA Jet Propulsion Laboratory operates under a contract with the National Aeronautics and Space Administration (80NM0018D0004). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Aeronautics and Space Administration (NASA) or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. Author contributions B.G. and C.M. envisioned the original research question and B.G. managed the analyses and organized the drafting process. L.M. and K.M. reviewed and edited drafts, and C.M. edited and contributed text. All authors reviewed the manuscript. The following elaborates on the roles and contributions of the authors in accordance with CrediT principles: Conceptualization: B.G., L.M., K.M., C.M.; Methodology: B.G.; Investigation: B.G..; Visualization: B.G.; Funding Acquisition: L.M., K.M., C.M.; Project Administration: L.M., K.M., C.M.; Supervision: C.M.; Writing - Original Draft: B.G., L.M., K.M., C.M.; Writing - Review & Editing: B.G., K.M., C.M. Data availability statement A comprehensive list of the datasets and code supporting the findings of this study is available in the Data Citations subsection below and openly accessible through the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Additional long-form background and methods are provided in the Supporting Information and in wiki format. The Jupyter Notebook used for pre-processing, synthesis, and analysis in the paper alongside an executable version in the cloud hosted on GitHub is made available at the links below. The codebase will be preserved at the JPL Open Repository (JOR). DOI: TBD https://github.com/bradleygay/geoengai/wiki https://github.com/bradleygay/geoengai/tree/master https://obscure-bassoon-547gxwvj4p42pvv4.github.dev/?editor=jupyter Datasets Repositories, data archives, simulation results, and synthesis work are publicly available (GitHub, JPL DOI). Much of the state, diagnostic, and prognostic variables are inferred and/or derived from global annual mean mixing ratios, harmonized emissions, global annual mean radiative forcing, SSP-based simulations and scenario projections (i.e., SSPs), and subsequent geoengineering experiments (e.g., GeoMIP, CDRMIP). The corresponding models, variables, and frequency are enumerated in the Supplementary Information. Activity Nodes CMIP5 Scenarios (RCPs): https://www.pik-potsdam.de/~mmalte/rcps CMIP6 Scenarios (SSPs): ScenarioMIP: https://aims2.llnl.gov/search CMIP6 Experiments (MIPs): CDRMIP, GeoMIP: https://aims2.llnl.gov/search Models ScenarioMIP (59 Models) ACCESS-CM2, ACCESS-ESM1-5, AWI-CM-1-1-MR, BCC-CSM2-MR, CAMS-CSM1-0, CAS-ESM2-0, CESM2, CESM2-FV2, CESM2-WACCM, CIESM, CMCC-CM2-SR5, CMCC-ESM2, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, CanESM5, CanESM5-1, CanESM5-CanOE, E3SM-1-0, E3SM-1-1,E3SM-1-1-ECA, E3SM-2-0, EC-Earth3, EC-Earth3-AerChem, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR, FGOALS-f3-L, FGOALS-g3, FIO-ESM-2-0, GFDL-CM4, GFDL-ESM4, GISS-E2-1-G, GISS-E2-1-G-CC, GISS-E2-1-H, GISS-E2-2-G, HadGEM3-GC31-LL, HadGEM3-GC31-MM, IITM-ESM, INM-CM4-8, INM-CM5-0, IPSL-CM5A2-INCA, IPSL-CM6A-LR, KACE-1-0-G, KIOST-ESM, MCM-UA-1-0, MIROC-ES2H, MIROC-ES2L, MIROC6, MPI-ESM-1-2-HAM, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NESM3, NorESM2-LM, NorESM2-MM, TaiESM1, UKESM1-0-LL , UKESM1-1-LL https://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/ScenarioMIP/ CDRMIP (12 Models) ACCESS-ESM1-5, CanESM5, CanESM5-CanOE, CAS-ESM2-0, CESM2, CNRM-ESM2-1, GFDL-ESM4, GISS-E2-1-G-CC, MIROC-ES2L, MPI-ESM1-2-LR, NorESM2-LM, UKESM1-0-LL https://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/CDRMIP/ GeoMIP (8 Models) CanESM5, CESM2-WACCM, CNRM-ESM2-1, IPSL-CM6A-LR, MIROC-ES2H, MPI-ESM1-2-HR, MPI-ESM1-2-LR, UKESM1-0-LL https://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/GeoMIP/ CDRMIP Experiments | UKESM1-0-LL r1i1p1f2 member 1pctCO2-cdr 1 percent per year decrease in CO 2 from 4xCO 2 http://doi.org/10.22033/ESGF/CMIP6.12183 ts: CMIP6.CDRMIP.MOHC.UKESM1-0-LL.1pctCO2-cdr.r1i1p1f2.Amon.ts.gn.v20200425|esgf.ceda.ac.uk pr: CMIP6.CDRMIP.MOHC.UKESM1-0-LL.1pctCO2-cdr.r1i1p1f2.Amon.pr.gn.v20200425|esgf.ceda.ac.uk [CH 4 ]: CMIP6.CDRMIP.MOHC.UKESM1-0-LL.1pctCO2-cdr.r1i1p1f2.Amon.ch4.gn.v20200425|esgf.ceda.ac.uk esm-1pct-brch-1000PgC Zero emissions simulation branched from 1percent run after 1000 PgC cumulative emission http://doi.org/10.22033/ESGF/CMIP6.10785 ts: CMIP6.C4MIP.MOHC.UKESM1-0-LL.esm-1pct-brch-1000PgC.r1i1p1f2.Amon.ts.gn.v20200210|esgf.ceda.ac.uk pr: CMIP6.C4MIP.MOHC.UKESM1-0-LL.esm-1pct-brch-1000PgC.r1i1p1f2.Amon.pr.gn.v20200210|esgf.ceda.ac.uk [CH 4 ]: CMIP6.C4MIP.MOHC.UKESM1-0-LL.esm-1pct-brch-1000PgC.r1i1p1f2.Amon.ch4.gn.v20200210|esgf.ceda.ac.uk GeoMIP Experiments | UKESM1-0-LL r1i1p1f2 ensemble member G1 Abrupt quadrupling of CO 2 plus reduction in total solar irradiance http://doi.org/10.22033/ESGF/CMIP6.5812 ts: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G1.r1i1p1f2.Amon.ts.gn.v20190916|esgf.ceda.ac.uk pr: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G1.r1i1p1f2.Amon.pr.gn.v20190916|esgf.ceda.ac.uk [CH 4 ]: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G1.r1i1p1f2.Amon.ch4.gn.v20190916|esgf.ceda.ac.uk G6Solar Total solar irradiance reduction to reduce net forcing from SSP585 to SSP245 http://doi.org/10.22033/ESGF/CMIP6.5820 ts: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6solar.r1i1p1f2.Amon.ts.gn.v20191031|esgf.ceda.ac.uk pr: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6solar.r1i1p1f2.Amon.pr.gn.v20191031|esgf.ceda.ac.uk [CH 4 ]: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6solar.r1i1p1f2.Amon.ch4.gn.v20191031|esgf.ceda.ac.uk G6Sulfur Stratospheric sulfate aerosol injection to reduce net forcing from SSP585 to SSP245 http://doi.org/10.22033/ESGF/CMIP6.5822 ts: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6sulfur.r1i1p1f2.Amon.ts.gn.v20191113|esgf.ceda.ac.uk pr: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6sulfur.r1i1p1f2.Amon.pr.gn.v20191113|esgf.ceda.ac.uk [CH 4 ]: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6sulfur.r1i1p1f2.Amon.ch4.gn.v20191113|esgf.ceda.ac.uk G7Cirrus Increase cirrus ice crystal fall speed to reduce net forcing in SSP585 by 1 W m-2 http://doi.org/10.22033/ESGF/CMIP6.5828 ts: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G7cirrus.r1i1p1f2.Amon.ts.gn.v20191125|esgf.ceda.ac.uk pr: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G7cirrus.r1i1p1f2.Amon.pr.gn.v20191125|esgf.ceda.ac.uk [CH 4 ]: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G7cirrus.r1i1p1f2.Amon.ch4.gn.v20191125|esgf.ceda.ac.uk Additional information Competing interests statement The authors declare this research was conducted without competing interests, commercial, and/or financial relationships that may be construed as a potential conflict of interest. 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Supplementary Files NCEGayS1.docx Cite Share Download PDF Status: Published Journal Publication published 09 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviews received at journal 18 Nov, 2024 Reviews received at journal 01 Nov, 2024 Reviewers agreed at journal 24 Oct, 2024 Reviewers agreed at journal 22 Oct, 2024 Reviewers invited by journal 08 Jul, 2024 Editor assigned by journal 05 Jul, 2024 Editor invited by journal 25 Jun, 2024 Submission checks completed at journal 24 May, 2024 First submitted to journal 23 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4469037","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":307202362,"identity":"cb72ab0d-6553-4378-af82-692fad9c6ac0","order_by":0,"name":"Bradley Gay","email":"data:image/png;base64,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","orcid":"","institution":"Jet Propulsion Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Bradley","middleName":"","lastName":"Gay","suffix":""},{"id":307202363,"identity":"38705494-aa68-40b9-89df-92f064fbc68e","order_by":1,"name":"Charles Miller","email":"","orcid":"","institution":"Jet Propulsion Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Miller","suffix":""},{"id":307202364,"identity":"3b19b1c7-718c-41be-8ca7-71123c58cfd3","order_by":2,"name":"Kimberley Miner","email":"","orcid":"","institution":"Jet Propulsion Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Kimberley","middleName":"","lastName":"Miner","suffix":""},{"id":307202366,"identity":"1c366d80-89a5-4cae-9e50-9a1c5c6eab01","order_by":3,"name":"Lukas Mandrake","email":"","orcid":"","institution":"Jet Propulsion Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Lukas","middleName":"","lastName":"Mandrake","suffix":""}],"badges":[],"createdAt":"2024-05-23 21:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4469037/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4469037/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-91195-6","type":"published","date":"2025-03-09T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57830407,"identity":"f03d389a-bb82-4046-8245-1000edc822b3","added_by":"auto","created_at":"2024-06-06 07:51:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":996769,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly means of 2-meter temperature and total precipitation were grouped by monthly time coordinates to compute global interannual weighted climatologies and departures from 1950-2022 (i.e., reference period: 1981-2010). Global departure variability is illustrated by both covariates, with sub-panel time series illustrating anomalous dynamics in \u003cstrong\u003eS1\u003c/strong\u003e. The variabilities exhibit increasing surface temperature and total precipitation of 1.374\u003cimg width=\"8\" height=\"13\" src=\"file:///C:/Users/khan07/AppData/Local/Temp/msohtmlclip1/01/clip_image002.gif\"/\u003e0.481ºC and 0.045\u003cimg width=\"8\" height=\"13\" src=\"file:///C:/Users/khan07/AppData/Local/Temp/msohtmlclip1/01/clip_image002.gif\"/\u003e0.567 mm day\u003csup\u003e-1\u003c/sup\u003e. Climatologies, departures, and statistics are available in \u003cstrong\u003eS1\u003c/strong\u003e and \u003cstrong\u003eS4\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/dde17f6a9b2e9f0bff92de2f.jpeg"},{"id":57830409,"identity":"0359bbb9-432f-4c64-bb88-5d9645387f86","added_by":"auto","created_at":"2024-06-06 07:51:54","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1288343,"visible":true,"origin":"","legend":"\u003cp\u003eThe first panel above illustrates weighted departures for annual periodicity of surface temperature (\u003cimg width=\"4\" height=\"13\" src=\"file:///C:/Users/khan07/AppData/Local/Temp/msohtmlclip1/01/clip_image002.gif\"/\u003eC), total precipitation (mm day\u003csup\u003e-1\u003c/sup\u003e), and [CH\u003csub\u003e4\u003c/sub\u003e] (ppb, 300-1000hPa) from the first CDRMIP experiment (i.e., \u003cem\u003e1pctCO2-cdr\u003c/em\u003e) simulated by UKESM1-0-LL. The second panel expands on departure variabilities (i.e., reference period: 1991-2020) constrained to the observational record (\u003cstrong\u003eS3\u003c/strong\u003e). Additional plots delineating on weighted and unweighted climatologies, departures, and temporal windowing for ERA5 intercomparisons (i.e., \u003cem\u003e1pctCO2-cdr\u003c/em\u003e, 1990-2149 [1990-2022]) are provided in \u003cstrong\u003eS2\u003c/strong\u003e and \u003cstrong\u003eS3\u003c/strong\u003e, with comprehensive statistics in \u003cstrong\u003eS5\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/679796c24b4ef83a286d0cb3.jpeg"},{"id":57831426,"identity":"9052255e-3531-428e-8211-7b17eacf4df6","added_by":"auto","created_at":"2024-06-06 07:59:54","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1299856,"visible":true,"origin":"","legend":"\u003cp\u003eThe first panel above illustrates weighted departures for annual periodicity of surface temperature (\u003cimg width=\"4\" height=\"13\" src=\"file:///C:/Users/khan07/AppData/Local/Temp/msohtmlclip1/01/clip_image002.gif\"/\u003eC), total precipitation (mm day\u003csup\u003e-1\u003c/sup\u003e), and [CH\u003csub\u003e4\u003c/sub\u003e] (ppb, 300-1000hPa) from the second CDRMIP experiment (i.e., \u003cem\u003eesm-1pct-brch-1000PgC\u003c/em\u003e) simulated by UKESM1-0-LL. The second panel expands on departure variabilities (i.e., reference period: 1991-2020) constrained to the observational record (\u003cstrong\u003eS3\u003c/strong\u003e). Additional plots delineating on weighted and unweighted climatologies, departures, and temporal windowing for intercomparisons (i.e., \u003cem\u003eesm-1pct-brch-1000PgC\u003c/em\u003e, 1950-2149 [1950-2022]) are provided in \u003cstrong\u003eS2\u003c/strong\u003e and \u003cstrong\u003eS3\u003c/strong\u003e, with comprehensive statistics in \u003cstrong\u003eS5\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/4c1ae4deac0a1ca6ee444154.jpeg"},{"id":57830408,"identity":"78667c5a-d843-48d5-ba35-2469951f4a62","added_by":"auto","created_at":"2024-06-06 07:51:54","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":635392,"visible":true,"origin":"","legend":"\u003cp\u003eThe panel above illustrates weighted mean departures for surface temperature (°C), precipitation flux (mm day\u003csup\u003e-1\u003c/sup\u003e), and sub-tropospheric multilevel mean of [CH\u003csub\u003e4\u003c/sub\u003e] (ppb, 300-1000hPa) from the \u003cem\u003efirst\u003c/em\u003e GeoMIP experiment (i.e., \u003cem\u003eG1\u003c/em\u003e) simulated by UKESM1-0-LL. Additional plots delineating on weighted and unweighted climatologies and departures are provided in \u003cstrong\u003eS2\u003c/strong\u003e and \u003cstrong\u003eS3\u003c/strong\u003e, with comprehensive statistics in \u003cstrong\u003eS5\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/e59c23c18df8e37b7efcd36a.jpeg"},{"id":57830414,"identity":"dc07cdaf-eeaa-4644-b830-31aeefd6fa3f","added_by":"auto","created_at":"2024-06-06 07:51:55","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1369078,"visible":true,"origin":"","legend":"\u003cp\u003eThe first panel above illustrates weighted mean departures for surface temperature (°C), precipitation flux (mm day\u003csup\u003e-1\u003c/sup\u003e), and sub-tropospheric multilevel mean of [CH\u003csub\u003e4\u003c/sub\u003e] (ppb, 300-1000hPa) from the second GeoMIP experiment (i.e., \u003cem\u003eG6Solar\u003c/em\u003e) simulated by UKESM1-0-LL.The second panel expands on departure variabilities (i.e., reference period: 1991-2020) constrained to the observational record (\u003cstrong\u003eS3\u003c/strong\u003e). Additional plots delineating on weighted and unweighted climatologies, departures, and temporal windowing for ERA5 intercomparisons (i.e., \u003cem\u003eG6Solar\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e2020-2022 [2020-2100]) are provided in \u003cstrong\u003eS2\u003c/strong\u003e and \u003cstrong\u003eS3\u003c/strong\u003e, with comprehensive statistics in \u003cstrong\u003eS5\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/3f7e6c05bb2b620742a0b5f5.jpeg"},{"id":57830412,"identity":"b9d701f8-987a-472a-b3af-da4a532e1432","added_by":"auto","created_at":"2024-06-06 07:51:55","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1084804,"visible":true,"origin":"","legend":"\u003cp\u003eThe first panel above illustrates weighted mean departures for surface temperature (°C), precipitation flux (mm day\u003csup\u003e-1\u003c/sup\u003e), and sub-tropospheric multilevel mean of [CH\u003csub\u003e4\u003c/sub\u003e] (ppb, 300-1000hPa) from the third GeoMIP experiment (i.e., \u003cem\u003eG6Sulfur\u003c/em\u003e) simulated by UKESM1-0-LL. The second panel expands on departure variabilities (i.e., reference period: 1991-2020) constrained to the observational record (\u003cstrong\u003eS3\u003c/strong\u003e). Additional plots delineating on weighted and unweighted climatologies, departures, and temporal windowing for ERA5 intercomparisons (i.e., \u003cem\u003eG6Sulfur\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e2020-2022 [2020-2100]) are provided in \u003cstrong\u003eS2\u003c/strong\u003e and \u003cstrong\u003eS3\u003c/strong\u003e, with comprehensive statistics in \u003cstrong\u003eS5\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/8e91fbc080c93be16bd67058.jpeg"},{"id":57830413,"identity":"3a1d0e92-2dcb-4a17-bf31-f368efc4e834","added_by":"auto","created_at":"2024-06-06 07:51:55","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1288710,"visible":true,"origin":"","legend":"\u003cp\u003eThe first panel illustrates weighted mean departures for surface temperature (°C), total precipitation (mm day\u003csup\u003e-1\u003c/sup\u003e), and sub-tropospheric multilevel mean of [CH\u003csub\u003e4\u003c/sub\u003e] (ppb, 300-1000hPa) from the \u003cem\u003efourth\u003c/em\u003e GeoMIP experiment (i.e., \u003cem\u003eG7Cirrus\u003c/em\u003e). The second panel expands on departure variabilities (i.e., reference period derived from ERA5 reanalysis: 1991-2020) constrained to the observational record (\u003cstrong\u003eS3\u003c/strong\u003e). Additional plots for climatologies, departures, statistics, and intercomparison temporal windowing (i.e., \u003cem\u003eG7Cirrus\u003c/em\u003e; 2020-2022 [2020-2100]) in \u003cstrong\u003eS2\u003c/strong\u003e, \u003cstrong\u003eS3\u003c/strong\u003e, and \u003cstrong\u003eS5\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/8dbff339e38a6e003d6816a0.jpeg"},{"id":57830411,"identity":"d4003705-3c39-428a-94e4-a57003e492f9","added_by":"auto","created_at":"2024-06-06 07:51:55","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":608839,"visible":true,"origin":"","legend":"\u003cp\u003eIn the top panel, simulation drift is illustrated, thus demonstrating the relationships between surface temperature and total precipitation derived from CDRMIP simulations relative to ERA5 reanalysis observations from 2020-2022. Below this panel, the bottom plots illustrate how these covariates (i.e., surface temperature, total precipitation) differ not only among various GeoMIP simulations and ERA5 reanalysis data, but also relative to the overlaying plots above, i.e., CDRMIP simulations.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/9bde90333ef84253e8939669.jpeg"},{"id":78190683,"identity":"65e95334-986e-4d51-aa0a-1df42e9a5f31","added_by":"auto","created_at":"2025-03-10 19:50:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9603264,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/25f80ba5-2d08-4a53-90c0-1b417336cb9a.pdf"},{"id":57830416,"identity":"3ac3107d-ae49-48d1-80e6-a288ded04a53","added_by":"auto","created_at":"2024-06-06 07:51:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":154669091,"visible":true,"origin":"","legend":"","description":"","filename":"NCEGayS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4469037/v1/7052a1476a1c65e0b02624fa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Earth System Responses to Climate Mitigation and Intervention with Scenario-Based Simulations and Data-Driven Insight","fulltext":[{"header":"Introduction","content":"\u003cp\u003eState-of-the-art climate models failed to explain why global temperatures spiked in 2023, with a 0.2\u0026deg;C divergence between expected and observed annual mean temperatures, suggesting that the world may be entering uncharted territory\u003csup\u003e1\u003c/sup\u003e. The climate crisis confronts us with an urgent challenge that requires decision-making under conditions of high uncertainty and incomplete information. Climate change is accelerating while anthropogenic greenhouse gas (GHG) emissions continue to rise, risking the ability of the global community to achieve key climate stabilization targets\u003csup\u003e2\u003c/sup\u003e. For decades, climate models warned of linear atmospheric warming and nonlinear changes to the land surface. Despite this warning, global net emissions of anthropogenic greenhouse gases reached record levels over the last decade\u003csup\u003e3\u003c/sup\u003e. Current projections suggest that, barring major mitigation efforts, the cumulative concentration of greenhouse gases is on course to eclipse the 1.5\u0026deg;C climate stabilization threshold by 2035, a stark acceleration from previous forecasts\u003csup\u003e4\u003c/sup\u003e. This anticipated exceedance of the 1.5\u0026deg;C threshold is expected to cross multiple climate system tipping points\u003csup\u003e5,6\u003c/sup\u003e. These tipping points have the potential to activate runaway feedbacks driving ecosystem transformation and disrupting global interconnections, with impacts forecasted to affect Circumpolar regions, tropical rainforests, and global atmospheric circulation and hydrologic systems.\u003csup\u003e1\u003c/sup\u003e. If left unabated, there is a 67% likelihood that current emissions trends could exhaust the total remaining carbon budget of the planet in less than a decade\u003csup\u003e7\u003c/sup\u003e. These events drive an increasing demand for global solutions to mitigate climate change. Geoengineering - the deliberate large-scale intervention in the Earth system to manipulate specific biogeochemical processes or elements of the physical climate to counteract the impacts of climate change\u003csup\u003e7,8,9\u003c/sup\u003e - presents an intricate problem: what strategies to implement and when to implement them. As the planet continues to warm and GHG emissions reduction and carbon removal methods fall short of their goals, more direct modification may be required to offset growing climate impacts. The deployment of geoengineering technologies appears increasingly inevitable\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe goal of recent geoengineering proposals is to reduce global temperatures and carbon dioxide ([CO\u003csub\u003e2\u003c/sub\u003e]) concentrations to pre-industrial levels\u003csup\u003e11,12,13,14\u003c/sup\u003e. Some methods involve offsetting climate change impacts by modifying the magnitude, rate, and extent of solar radiation in the form of solar radiation management (SRM)\u003csup\u003e15\u003c/sup\u003e. Other strategies may bolster the natural sequestration of CO\u003csub\u003e2\u003c/sub\u003e by enhancing carbon capture and sequestration through rock weathering\u003csup\u003e16\u003c/sup\u003e, afforestation, carbon aerosol capture, biochar, and ocean fertilization\u003csup\u003e16\u003c/sup\u003e. Alternatively, market-based instruments include carbon offset incentivization and global carbon accounting approaches (e.g., renewable energy credits). SRM strategies involve the injection of sulfate aerosols into the stratosphere to mitigate climate change and achieve regional climate balance by reflecting sunlight to cool the planet\u003csup\u003e18\u003c/sup\u003e. Aside from the feedback repercussions associated with this approach (i.e., amplified ocean acidification)\u003csup\u003e19\u003c/sup\u003e, large uncertainties propagate from this intervention strategy\u003csup\u003e20\u003c/sup\u003e. These uncertainties originate from the assumption of a uniform distribution of aerosols in both hemispheres; in reality, adopting SRM methods could precipitate an uneven spatiotemporal distribution of cooling effects across different latitudes and seasons with unknown secondary and tertiary consequences\u003csup\u003e10,11\u003c/sup\u003e. Stratospheric aerosol injection (SAI) remains a potential climate intervention mechanism to reduce surface temperatures\u003csup\u003e21,22,23,24\u003c/sup\u003e; however, SAI deployment may result in catastrophic side effects\u003csup\u003e25\u003c/sup\u003e including reduced productivity or global breadbasket collapse\u003csup\u003e25,26,27\u003c/sup\u003e. In addition to aerosol injection, SRM proposals include deploying space reflectors and a space-based solar shield\u003csup\u003e28\u003c/sup\u003e, thinning cirrus clouds\u003csup\u003e29\u003c/sup\u003e, brightening marine clouds\u003csup\u003e30\u003c/sup\u003e, and more recently proposed, injecting ice particles 17 km above the earth surface to facilitate stratospheric dehydration\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccording to the World Meteorological Organization (WMO), weather modification to mitigate climate warming are in operation today, with approximately 50 countries and nine US states deploying regional climate engineering projects without regulation via interstate provisioning, e.g., cloud seeding (i.e., rain enhancement)\u003csup\u003e32\u003c/sup\u003e. Cloud seeding operations are gaining momentum, which began nearly 65 years ago in China to induce rainfall after extended periods of heatwaves and drought\u003csup\u003e33\u003c/sup\u003e. Cirrus cloud seeding aids in precipitation formation by injecting silver iodide into the lower atmosphere to mimic ice crystallinity while modifying the physical chemistry of super-cooled mixed phase clouds, is gaining momentum\u003csup\u003e34,35,36\u003c/sup\u003e. Modeling studies integrating global circulation models (e.g., ECHAM6-HAM, CESM-CAM5) and scenario-specific forcing data explore the feasibility of these cloud seeding strategies and the associated impacts on radiative and climate response patterns. In particular, results from cirrus cloud seeding simulations demonstrate a reduction in cirrus-induced radiative effect (-0.8 to -1.8Wm\u003csup\u003e-2\u003c/sup\u003e), a reduction in CO\u003csub\u003e2\u003c/sub\u003e-induced climate change (50\u0026ndash;85%), and mixed effects and uncertainty for extreme precipitation events\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGeoengineering techniques often involve searching physical parameters beyond the current observational record, thus overlooking data-driven forecasts (e.g., higher temperatures, stronger convection, increased aerosol load). Data-driven approaches are interpolative methods that require training datasets that span relevant spatial, temporal, and physical parameter ranges occupied by the input and prediction sets. Despite their promise, data-driven approaches to speculative scenarios may introduce additional risks and biases that emerge from fundamental assumptions as well as disjointed standards of practice. Accordingly, the uninformed application of data-driven methods can manifest into unintended risks and outcomes difficult to detect or diagnose even under manual inspection by domain experts\u003csup\u003e38,39\u003c/sup\u003e. Such oversight may originate from the failure to rigorously assess model performance with sufficient data while supporting interpretability, explainability, and transparency\u003csup\u003e40\u003c/sup\u003e. How do we harness data and state-of-the-art tools to address the fundamental challenges associated with geoengineering? What is the most optimal use of these techniques with intervention strategies given an incomplete observational record in addition to an imperfect knowledge and understanding of the earth system and associated feedbacks\u003csup\u003e41\u003c/sup\u003e? What are the risks and unintended outcomes from these decisions? These solutions are not trivial.\u003c/p\u003e \u003cp\u003eGiven the evolving complexities and multidimensional dynamics of the Earth system, it is crucial to employ multimodal data and modeling frameworks that match the challenge in complexity and nuance. Utilizing existing observational systems and nascent technology will allow countries to monitor, interpret, and better understand the biogeochemical processes, synergistic relationships, and spatiotemporal variability governing the Earth system. We propose leveraging reanalysis products and model simulations may help optimize mitigation logistics, bridge multiscale intervention challenges, summarize contingency scenarios, and support continuous monitoring infrastructure. In this study, we examine various mitigation and geoengineering strategies and the subsequent impacts on the earth system by using historical estimates from the fifth-generation land-specific global atmospheric reanalysis data subset (i.e., ERA5\u003csup\u003e42\u003c/sup\u003e) and a series of mitigation and intervention experiments supporting the Coupled Model Intercomparison Project, Phase 6 (i.e., CMIP6). Mitigation experiments are derived from the Carbon Dioxide Removal Model Intercomparison Project (i.e., CDRMIP\u003csup\u003e43\u003c/sup\u003e) while geoengineering intervention experiment simulations are derived from the Geoengineering Model Intercomparison Project (i.e., GeoMIP\u003csup\u003e44,45,46\u003c/sup\u003e). By utilizing these resources effectively, we can observe how global and regional climate baselines are changing and how the Earth and land-atmospheric responses will respond to mitigation and intervention deployment\u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eERA5 Reanalysis\u003c/h2\u003e\n\u003cp\u003eFor \u003cem\u003eretrospective\u003c/em\u003e analysis, we examined high-resolution 2-meter temperature (t2m) and total precipitation (tp) surface variables derived from the ECMWF ERA5 Monthly-Averaged Climate Reanalysis dataset over a 73-year period (i.e., 1950\u0026ndash;2022). This data product provides global physics-based data-driven land surface post-processed monthly-resampled observations on a 31-km rectilinear latitude-longitude grid (i.e., \u003cem\u003eMethods\u003c/em\u003e). The results below illustrate mean departures computed from monthly weighted climatologies with temporal thresholding (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eResults indicate that climatology varied substantially from the observed record over this 73-year period, with Northern Siberia and the Horn of Africa experiencing an increase in surface temperature by as much as 7.626\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC between 1950 and 2022 and surface temperature reductions in Central Africa, Southern Australia, Eastern Brazil, and Northwestern Mexico by -2.378\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC; however, global mean surface temperatures steadily increased by 1.374\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.481\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC. Moreover, regional surface temperature anomalies during this period ranged from \u0026minus;\u0026thinsp;1.332\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC across the Canadian Boreal and Northern Siberia (e.g., Yamalo-Nenets Autonomous Okrug) to 1.192\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC across the Arctic (e.g., Greenland, Northwestern Alaska, Chukotka Autonomous Okrug), Antarctica, South America (e.g., Uruguay), Africa (e.g., Angola), and Southeastern China. Additionally, global mean surface temperature departures accounted for a significant reduction of -0.297\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.331\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC. When resampled, these global and regional patterns remained intact; however, the statistical ranges varied; specifically, regional surface temperature mean variability (i.e., departures) ranged between \u0026minus;\u0026thinsp;2.334-4.533\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, with increasing global mean surface temperature variability, i.e., 0.177\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.590\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC. Precipitation patterns from 1950 to 2022 indicate increased total precipitation by 1.170 mm day\u003csup\u003e-1\u003c/sup\u003e in Southern Alaska (e.g., Chugach and Copper River), the northern coast of Norway, South America (e.g., Western Ecuador, Northern Brazil), Southern India, Northwestern Indonesia, and Africa (e.g., Central Africa Republic, Democratic Republic of the Congo, Angola, South Africa), total precipitation reduction in South America (e.g., Colombia, Bolivia) and East Africa (e.g., Sudan, Ethiopia) by -1.195 mm day\u003csup\u003e-1\u003c/sup\u003e, and a marginal increase in global mean total precipitation of 0.045\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.567 mm day\u003csup\u003e-1\u003c/sup\u003e. Furthermore, regional precipitation departures indicate a reduction of -0.393 mm day\u003csup\u003e-1\u003c/sup\u003e across the Amazon, Sahel, and Southeast Asia, while increasing total precipitation anomalies were quantified to nearly 0.125 mm day\u003csup\u003e-1\u003c/sup\u003e in Southeastern Africa (e.g., Zimbabwe, Mozambique). Reductions in global mean total precipitation anomalies were computed, i.e., -0.029\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.054 mm day\u003csup\u003e-1\u003c/sup\u003e. Similar to surface temperature variability after resampling indicated above, regional precipitation departures ranged from \u0026minus;\u0026thinsp;5.109\u0026ndash;5.546 mm day\u003csup\u003e-1\u003c/sup\u003e, with global mean precipitation departures of 0.045\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.567 mm day\u003csup\u003e-1\u003c/sup\u003e (i.e., increasing precipitation, P\u0026thinsp;\u0026gt;\u0026thinsp;ET).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eCMIP6 | CDRMIP Simulations\u003c/h3\u003e\n\u003cp\u003eFor prognostication and simulation purposes, \u003cem\u003etwo\u003c/em\u003e CO\u003csub\u003e2\u003c/sub\u003e removal (i.e., CDR) mitigation experiments were simulated with the UKESM0-1-LL model including (\u003cstrong\u003e1a\u003c/strong\u003e) \u003cem\u003eafter\u003c/em\u003e abrupt quadrupling of CO\u003csub\u003e2\u003c/sub\u003e, instantiate one percent CO\u003csub\u003e2\u003c/sub\u003e reduction per year, i.e., \u003cem\u003e1pctCO2-cdr\u003c/em\u003e and (\u003cstrong\u003e1b\u003c/strong\u003e) continuation of zero emission simulation branch from \u003cem\u003e1pctCO2-cdr\u003c/em\u003e after 1000PgC cumulative emissions threshold achieved, i.e., \u003cem\u003eesm-1pct-brch-1000PgC\u003c/em\u003e. We employed additional historical and future controls of emissions reduction into the model simulations for \u003cem\u003eretrospective\u003c/em\u003e and \u003cem\u003eprognostic\u003c/em\u003e purposes. Concluding simulation analyses, intercomparisons are facilitated to identify discrepancies between these mitigation and intervention simulations relative to the resampled ERA5 observational record.\u003c/p\u003e\n\u003ch3\u003e1a. 1pctCO2-cdr\u003c/h3\u003e\n\u003cp\u003eDuring this idealized experiment, CO\u003csub\u003e2\u003c/sub\u003e removal methods to reduce 4xCO\u003csub\u003e2\u003c/sub\u003e baseline levels by one percent per year until preindustrial control (i.e., PiC) is obtained \u003cem\u003eand\u003c/em\u003e maintained, with \u003cem\u003eincreases\u003c/em\u003e in global weighted mean variability (i.e., detrending via annual climatology) of surface temperature (3.885\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e2.624\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC), total precipitation (0.174\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.781 mm day\u003csup\u003e-1\u003c/sup\u003e), and [CH\u003csub\u003e4\u003c/sub\u003e] (0.013\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.269 ppb) over the 1990\u0026ndash;2149 time period (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Interestingly, the global weighted mean variability further condenses these trends to \u003cem\u003edecreasing\u003c/em\u003e surface temperature and total precipitation (-1.909\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.216\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, -0.263\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.125 mm day\u003csup\u003e-1\u003c/sup\u003e), while [CH\u003csub\u003e4\u003c/sub\u003e] \u003cem\u003eincreases\u003c/em\u003e (0.031\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.015 ppb).\u003c/p\u003e\n\u003cp\u003eThe most compelling relationship with this first experiment is the clear indication of pronounced Arctic amplification and widespread warming (-0.245-13.078\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC), with hotspots near Hudson Bay, the Bering Sea, Baffin Bay, Eastern Europe, and the Middle East; in addition, amplified [CH\u003csub\u003e4\u003c/sub\u003e] variability occurred along the Antarctic Peninsula coastline (-2.545-2.871 ppb), with pronounced warming surrounding Ellsworth Land, the Ross Ice Shelf, and north of Queen Maud Land. In contrast, large regions of the South Pacific Ocean and Indian Ocean demonstrated clear indications of cooling, potentially driven by the deep-water formation, westerlies, and surface currents, i.e., west wind drift from the Antarctic Circumpolar Current and the Peru Current and possibly catalyzed by the Northern Subpolar Gyre collapse as indicated by the quiescence of precipitation variability across the mid-latitudes. The most variability in total precipitation may be attributed to ocean-atmospheric equatorial interactions in the Arabian Sea (-12.228-9.064 mm day\u003csup\u003e-1\u003c/sup\u003e), Marshall Islands, and Eastern Bolivia.\u003c/p\u003e\n\u003cp\u003eTo differentiate regional patterns over this period, we examined the historical relationships between simulation\u003c/p\u003e\n\u003cp\u003eoutputs of surface temperature and total precipitation derived from this \u003cem\u003e1pctCO2-cdr\u003c/em\u003e experiment in alignment with the temporal windowing characterizing the ERA5 reanalysis observational record (i.e., 1990\u0026ndash;2022, \u003cstrong\u003eS5\u003c/strong\u003e). Most significantly, these tri-decadal simulations predominately \u003cem\u003eoverestimate\u003c/em\u003e global weighted anomalies (6.878\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.007\u0026ordm;C) and marginally \u003cem\u003eunderestimate\u003c/em\u003e global weighted total precipitation (-0.102\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.003 mm day\u003csup\u003e-1\u003c/sup\u003e), with subsequent weighted mean departures for surface temperature and total precipitation (3.784\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e2.004\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, 0.431\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.906 mm day\u003csup\u003e-1\u003c/sup\u003e). Furthermore, surface temperature and total precipitation \u003cem\u003edecreases\u003c/em\u003e (-0.010\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.365\u0026ordm;C, -0.024\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.892 mm day\u003csup\u003e-1\u003c/sup\u003e) while [CH\u003csub\u003e4\u003c/sub\u003e] \u003cem\u003eincreases\u003c/em\u003e (0.018\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.251 ppb) over this period. Moreover, we examined global weighted departures, noting surface temperature and [CH\u003csub\u003e4\u003c/sub\u003e] \u003cem\u003eincreases\u003c/em\u003e (0.013\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.723\u0026ordm;C, 0.033\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.234 ppb) while total precipitation \u003cem\u003edecreases\u003c/em\u003e (-0.089\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.391 mm day\u003csup\u003e-1\u003c/sup\u003e) over this 33-year window.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e1b. esm-1pct-brch-1000PgC\u003c/h2\u003e\n\u003cp\u003eFor the next experiment, a zero emissions scenario is emulated from the previous \u003cem\u003e1pctCO2-cdr\u003c/em\u003e experiment, with [CO\u003csub\u003e2\u003c/sub\u003e] removal continuing beyond achieving the 1000Pg threshold for cumulative emissions from 1950\u0026ndash;2149. Over the course of 200 years of continued [CO\u003csub\u003e2\u003c/sub\u003e] reduction, the global mean climatologies characterizing all three covariates \u003cem\u003eincreased\u003c/em\u003e, with surface temperatures rising 0.061\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.347\u0026ordm;C, total precipitation increasing by 0.003\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.331 mm day\u003csup\u003e-1\u003c/sup\u003e and elevating [CH\u003csub\u003e4\u003c/sub\u003e] by 0.001\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.169 ppb (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Alternatively, the global weighted mean variability exhibited by all three covariates \u003cem\u003edecreased\u003c/em\u003e, with surface temperature departures ranging from \u0026minus;\u0026thinsp;5.446-4.854\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, total precipitation variability from \u0026minus;\u0026thinsp;5.404\u0026ndash;5.218 mm day\u003csup\u003e-1\u003c/sup\u003e, and [CH\u003csub\u003e4\u003c/sub\u003e] departures ranging from \u0026minus;\u0026thinsp;1.729\u0026ndash;2.519 ppb. This coupled relationship is further illustrated by the mean of global weighted mean variability computed for each covariate: -0.170\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.830\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, -0.011\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.665 mm day\u003csup\u003e-1\u003c/sup\u003e, and \u0026minus;\u0026thinsp;0.001\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.188 ppb, respectively.\u003c/p\u003e\n\u003cp\u003eWe evaluated regional changes in mean covariability over time. Results suggest warming is less pronounced and more stochastic across the globe, with increasing surface temperature variability indicated west of the Antarctic Peninsula and distributed across the Norwegian and Greenland Sea, extending into the Arctic Ocean (4.854\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC). Increasing precipitation departures and cluster densities were localized to equatorial regions of Eastern Brazil, French Polynesia, Arabian Sea and Bay of Bengal, Northern Vietnam, and east of the Coral Sea (7.471 mm day\u003csup\u003e-1\u003c/sup\u003e). In addition, increased variability of [CH\u003csub\u003e4\u003c/sub\u003e] mean was observed in the Komi Republic, isolated anomalies near the Northwestern Passages, and relatively minor plume developments near the Amundsen Sea coast and between Wilkes and Victoria Land (2.519 ppb). In contrast, regional mean variability of surface temperature decreased near the Kara and Laptev Sea, Canadian boreal, and Amery Ice Shelf in Antarctica (-5.446\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC) while [CH\u003csub\u003e4\u003c/sub\u003e] departures decreased near Bellingshausen Sea and off the coast of Eastern Antarctica (-1.729 ppb). Bands of total precipitation variability are distributed across equatorial Oceania near the Hawaiian Islands, off the coast of Eastern Brazil (i.e., Pernambuco), and north of the Solomon Islands and Papua New Guinea (-7.383 mm day\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eHistorical simulations from the \u003cem\u003eesm-1pct-brch-1000PgC\u003c/em\u003e experiment were compared with the ERA5 observational record, ensuring temporal alignment over a 73-year period (i.e., 1950\u0026ndash;2022) wherein zero emissions were emulated from the \u003cem\u003e1pctCO2-cdr\u003c/em\u003e run after achieving the cumulative emissions 1000Pg threshold (\u003cstrong\u003eS3\u003c/strong\u003e). During the intercomparison, simulations \u003cem\u003eoverestimate\u003c/em\u003e global weighted mean of surface temperature (2.883\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.070\u0026ordm;C) and \u003cem\u003eunderestimate\u003c/em\u003e total precipitation (-0.101\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.003 mm day\u003csup\u003e-1\u003c/sup\u003e). In addition, global climatologies and weighted mean variabilities were computed, resulting in the following climatologies: surface temperature (-0.032\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.299\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, with ranges between \u0026minus;\u0026thinsp;3.197-3.444\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC), total precipitation (0.001\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.330 mm day\u003csup\u003e-1\u003c/sup\u003e, ranging from \u0026minus;\u0026thinsp;3.618\u0026ndash;2.374 mm day\u003csup\u003e-1\u003c/sup\u003e), and [CH\u003csub\u003e4\u003c/sub\u003e] (0.007\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.155 ppb, with ranges \u0026minus;\u0026thinsp;1.631\u0026ndash;1.808 ppb). Thereafter, global weighted mean departures were computed and indicate simulations \u003cem\u003eoverestimated\u003c/em\u003e surface temperature (0.051\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.610\u0026ordm;C) and [CH\u003csub\u003e4\u003c/sub\u003e] (0.010\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.172 ppb) while narrowly \u003cem\u003eunderestimating\u003c/em\u003e total precipitation (-0.0003\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.583 mm day\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eCMIP6 | GeoMIP Simulations\u003c/h3\u003e\n\u003cp\u003eSimilarly, model simulations were conducted by the UKESM project with associated earth system model components (e.g., aerosol, atmospheric and oceanic general circulation, and biogeochemical models) across \u003cem\u003efour\u003c/em\u003e geoengineering experiments for both \u003cem\u003eretrospection\u003c/em\u003e and \u003cem\u003eprognostication\u003c/em\u003e. These \u003cem\u003efour\u003c/em\u003e experiments and their corresponding climatological and departure variability \u0026ndash; temperature, precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] anomalies differing from the long-term global climatological mean. These experiments include (\u003cstrong\u003e2a\u003c/strong\u003e) an abrupt quadrupling of CO\u003csub\u003e2\u003c/sub\u003e while simultaneously integrating solar irradiance reduction, i.e., solar dimming (i.e., \u003cem\u003eG1\u003c/em\u003e), (\u003cstrong\u003e2b\u003c/strong\u003e) high-to-medium solar net forcing reduction from SSP585 to SSP245 (i.e., \u003cem\u003eG6Solar\u003c/em\u003e), (\u003cstrong\u003e2c\u003c/strong\u003e) stratospheric sulfate aerosol injection to reduce net radiative forcing from SSP585 to SSP245 (i.e., \u003cem\u003eG6Sulfur\u003c/em\u003e), and (\u003cstrong\u003e2d\u003c/strong\u003e) cirrus cloud seeding to reduce net radiative forcing from SSP585 by 1 Wm\u003csup\u003e-2\u003c/sup\u003e (i.e., \u003cem\u003eG7Cirrus\u003c/em\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e2a. G1\u003c/h2\u003e\n\u003cp\u003eTo establish a historical baseline, the first intervention experiment simulated the instantaneous quadrupling of atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration while simultaneously applying solar irradiance reduction (i.e., dimming) strategies. The results illustrate global surface temperature, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] weighted mean variability from 1850\u0026ndash;1949, ranging between \u0026minus;\u0026thinsp;6.873-4.377\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, -5.795-8.144 mm day\u003csup\u003e-1\u003c/sup\u003e, and \u0026minus;\u0026thinsp;0.004-2.200 ppb respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Moreover, the global climatological mean of surface temperature, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] remained relatively stable, with surface temperature and total precipitation \u003cem\u003edecreasing\u003c/em\u003e from \u0026minus;\u0026thinsp;0.010\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.472\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC and \u0026minus;\u0026thinsp;0.010\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.321 mm day\u003csup\u003e-1\u003c/sup\u003e while [CH\u003csub\u003e4\u003c/sub\u003e] \u003cem\u003eincreased\u003c/em\u003e over the 100-year period, i.e., 0.003\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.180 ppb. This homeostatic behavior demonstrates a relatively balanced net radiation budget in response to reducing the solar constant by offsetting longwave radiative impacts with fast radiative responses and reducing shortwave radiation via stratospheric adjustments.\u003c/p\u003e\n\u003cp\u003eFrom a regional context, global surface temperature mean variability increased from 1850\u0026ndash;1949 (0.100\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.847\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC), with increased surface temperature departures displayed in the Middle East, Northern Australia (i.e., Queensland), Shandong, Sendai Bay, and near several Antarctic ice shelves including the Ronne Ice Shelf, Ross Ice Shelf, and Amery Ice Shelf, all demonstrating increased warming trends and pronounced \u0026lsquo;hotspot\u0026rsquo; activity and covariability (4.377\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC). However, significant reductions in surface temperature variability were indicated primarily across the Chukchi and Beaufort Sea, as well as \u0026lsquo;mirroring\u0026rsquo; the Norwegian Atlantic Current period of the Atlantic Meridional Overturning Circulation near divergence in the Iceland Basin (0.151 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC). Over a period of 100 years, total precipitation variability increased less steadily with total incremental precipitation mean equivalent to 0.022\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.812 mm day\u003csup\u003e-1\u003c/sup\u003e across regional surfaces. Examining the regional patterns of global precipitation mean departures, Micronesia, Uruguay, and Bangladesh experienced the highest magnitude relative to significant drying in Southeast Asia and eastern Brazil (-1.911-8.144 mm day\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eTo inform baselines with carbon flux from \u003cem\u003eG1\u003c/em\u003e simulations, the historical evolution of global mean [CH\u003csub\u003e4\u003c/sub\u003e] experienced abrupt pulses of increasing emissions yet maintained a marginal net sink progressively from 1850\u0026ndash;1949, terminating with a net concentration difference of -0.001\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.206 ppb. High concentrations of atmospheric carbon aggregated across Scandinavia, Northwestern Siberia, and the coastline west of the Antarctic Peninsula near the Ross Ice Shelf and the Amundsen Sea (2.200 ppb), with noticeable opposing reductions in global atmospheric [CH\u003csub\u003e4\u003c/sub\u003e] variability exhibited along the eastern coastline of Antarctic coastline, emanating northeasterly from the Amery Ice Shelf into the Indian Ocean (-1.911 ppb). Intercomparisons between resampled ERA5 reanalysis data and \u003cem\u003eG1\u003c/em\u003e simulations were not conducted due to the temporal misalignment, i.e., ERA5 (1950\u0026ndash;2022) v. \u003cem\u003eG1\u003c/em\u003e (1850\u0026ndash;1949). However, after detrending \u003cem\u003eG1\u003c/em\u003e climatology to compute global mean departures from 1850\u0026ndash;1949, surface temperature and total precipitation departures \u003cem\u003edecreased\u003c/em\u003e from 1950\u0026ndash;2022 relative to 1850\u0026ndash;1949 (-0.242\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.0.326\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, -0.166\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.768 mm day\u003csup\u003e-1\u003c/sup\u003e), with a range of -2.967-4.624\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC and \u0026minus;\u0026thinsp;8.598\u0026ndash;2.262 mm day\u003csup\u003e-1\u003c/sup\u003e, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2b. G6Solar\u003c/h3\u003e\n\u003cp\u003eThe second geoengineering experiment simulated high-to-medium solar forcing reduction efforts via solar irradiance curtailment (i.e., SSP585 to SSP245, 2020\u0026ndash;2100). The radiative transfer dynamics evolve in response to instantiated variables, forcings, drivers, and parameterization in the model. In essence, this experiment illustrates how solar geoengineering facilitates cooling effects and the resulting maps and metrics demonstrate its utility and veracity for exploration and deployment. Global weighted climatological means for surface temperature, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] increased marginally from 2020\u0026ndash;2100 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), ranging from \u0026minus;\u0026thinsp;1.146\u0026ndash;6.593\u0026ordm;C (1.275\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.262\u0026ordm;C), -2.622-5.256 mm day\u003csup\u003e-1\u003c/sup\u003e (0.045\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.347 mm day\u003csup\u003e-1\u003c/sup\u003e), and \u0026minus;\u0026thinsp;4.612\u0026ndash;7.120 ppb (1.575\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.075 ppb). Weighted mean variability of global surface temperature and total precipitation from 2020 to 2100 ranged between \u0026minus;\u0026thinsp;14.479-1.615\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, -10.523-3.915 mm day\u003csup\u003e-1\u003c/sup\u003e, and \u0026minus;\u0026thinsp;11.216\u0026ndash;0.643 ppb, with a \u003cem\u003ereduction\u003c/em\u003e in global mean variability for surface temperature, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] as indicated by -2.545\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e2.605\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, -0.083\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.612 mm day\u003csup\u003e-1\u003c/sup\u003e, and \u0026minus;\u0026thinsp;4.368\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.623 ppb.\u003c/p\u003e\n\u003cp\u003eAccording to model simulations from this experiment, a number of regions exhibit hotspots for elevated surface temperature and [CH\u003csub\u003e4\u003c/sub\u003e] departures from planetary homeostasis, i.e., climatological norm. These hotspots include surface temperature anomalies in Northern and Southeastern Brazil, Northern coastlines of Nunavut, western coast of Baffin Bay, Eastern Europe, Northern Siberia, South Australia, Gujarat, and Indonesia. Aside from the clear equatorial gravitation for precipitation patterns, Uruguay exhibits periods of extended precipitation consistency. Alternatively, this intervention strategy introduces the concept of \u0026lsquo;cooling\u0026rsquo; down the earth: in terms of \u0026lsquo;cold spots\u0026rsquo; indicated by these results, some spatial patterns emerge. In particular, these departures illustrate how the climate is changing and moving away from climatological norms of the past. Though mostly constrained to open water in high latitudes, some of these cold spots include regions in the Bering Sea, Hudson Bay, Barents Sea, and extensions westward into the Greenland and Norwegian Sea.\u003c/p\u003e\n\u003cp\u003eAbiotic simulations predominately \u003cem\u003eunderestimate\u003c/em\u003e global weighted mean variability of surface temperature (-0.165\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.595\u0026ordm;C), total precipitation (-0.008\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.653 mm day\u003csup\u003e-1\u003c/sup\u003e), and [CH\u003csub\u003e4\u003c/sub\u003e] (-0.136\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.561 ppb). We examined the resulting global mean temporal differencing and covariability between historical observations from resampled ERA5 reanalysis data with \u003cem\u003eG6Solar\u003c/em\u003e simulations (i.e., 2020\u0026ndash;2022). During ERA5 and \u003cem\u003eG6Solar\u003c/em\u003e model intercomparisons of surface temperature and total precipitation from 2020\u0026ndash;2022, global weighted mean of mean differencing and standardized metrics were computed by spatially weighting the anomalies (i.e., weighted), removing climatology trends (i.e., unweighted), and applying a global mean operation across each observation and prediction originating from the reanalysis or simulation output (\u003cstrong\u003eS3\u003c/strong\u003e). Thereafter, the simulations from 2020\u0026ndash;2022 indicated \u003cem\u003eoverestimations\u003c/em\u003e of surface temperature on the order of 0.017\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.064\u0026ordm;C and \u003cem\u003eunderestimated\u003c/em\u003e total precipitation variability by -0.102\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.001 mm day\u003csup\u003e-1\u003c/sup\u003e. Interestingly, from 2020\u0026ndash;2022, this upward-trending pattern for global mean surface temperature and total precipitation variability was observed and analyzed with respect to overestimation magnitudes. Furthermore, precipitation experiences intrinsic dynamic shifts in regime\u003csup\u003e48\u003c/sup\u003e; therefore, temporal subsetting isolated trends for validation and sensitivity analyses. From 2020\u0026ndash;2021, global weighted mean variability differencing of surface temperature and total precipitation from \u003cem\u003eG6Solar\u003c/em\u003e simulations and ERA5 were \u003cem\u003eoverestimated\u003c/em\u003e (1.981\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.031\u0026ordm;C, 0.855\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.004 mm day\u003csup\u003e-1\u003c/sup\u003e) while both covariates subsequently \u003cem\u003eoverestimated\u003c/em\u003e across the 2021\u0026ndash;2022 period, i.e., differencing resulted in surface temperature and total precipitation mean variability of 2.030\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.080\u0026ordm;C and 0.881\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.030 mm day\u003csup\u003e-1\u003c/sup\u003e, respectively, yielding net overestimations in surface temperature and total precipitation over this three-year validation window.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e2c. G6Sulfur\u003c/h2\u003e\n\u003cp\u003eStratospheric aerosol injection is simulated in this experiment (i.e., 2020\u0026ndash;2100) to demonstrate the variability of forcing mechanisms and consequential impacts on the Earth system. Steady \u003cem\u003eincreases\u003c/em\u003e in global mean climatologies of surface temperature, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] are represented by ranges of -1.051-6.409\u0026ordm;C (1.346\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.302\u0026ordm;C), -2.223-3.126 mm day\u003csup\u003e-1\u003c/sup\u003e (0.033\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.327 mm day\u003csup\u003e-1\u003c/sup\u003e), and \u0026minus;\u0026thinsp;4.432\u0026ndash;6.376 ppb (1.372\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.175 ppb) until the end of the century (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Interestingly, all three covariates collectively experience \u003cem\u003ereductions\u003c/em\u003e in global weighted mean variabilities, i.e., departures between the bounds of 2020\u0026ndash;2100 (-2.701\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e2.635\u0026ordm;C, -0.060\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.653 mm day\u003csup\u003e-1\u003c/sup\u003e, and \u0026minus;\u0026thinsp;4.633\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.993 ppb), illustrating persistent reductions in global mean surface temperatures and atmospheric mean concentration, though erratic oscillations prompt marginal reductions in total precipitation. Additional marked increases in global weighted mean variability for total precipitation variability across the entirety of Eastern Brazil \u0026ndash; from Bahia to S\u0026atilde;o Paulo \u0026ndash; as well as surface temperature and [CH\u003csub\u003e4\u003c/sub\u003e] anomalies near the Ross and Amery Ice Shelf.\u003c/p\u003e\n\u003cp\u003eComparing ERA5 reanalysis data with historical simulations of SAI, a marked uptick in global mean temperatures is apparent prior to climatology detrending, i.e., increasing warming trend is more explicit, suggesting potential feedback interactions manifesting and/or contributing to amplified overestimations of water cycle covariates, notably during shoulder or transition seasons. Contemporary and CMIP6-derived simulation projections \u003cem\u003eoverestimate\u003c/em\u003e the magnitudes of some of these marginal/fringe oscillations, with future projections (i.e., 2023\u0026ndash;2100) of high-to-medium reduction (i.e., SSP585 to SSP245) via SAI demonstrating an even greater propensity to \u003cem\u003eoverestimate\u003c/em\u003e these covariates with large margins of magnitude differences. Therefore, temporal alignment with the observational record from ERA5 reanalysis during 2020\u0026ndash;2022 allows observation-driven data and simulation output intercomparison to identify any correlations, relationships, or behaviors that manifest and contribute to global change (\u003cstrong\u003eS3\u003c/strong\u003e). Computing these metrics and global mean variabilities (i.e., departures) via climatology removal and spatial averaging, it is determined that model simulations exhibit wide variability in covariate ranges (-2.149-1.558\u0026ordm;C, -5.713-3.561 mm day\u003csup\u003e-1\u003c/sup\u003e, -3.564-2.003 ppb); however, on average, these simulations \u003cem\u003eunderestimate\u003c/em\u003e global weighted mean variabilities of all covariates, i.e., surface temperature (-0.002\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.430\u0026ordm;C), total precipitation (-0.165\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.595 mm day\u003csup\u003e-1\u003c/sup\u003e), and [CH\u003csub\u003e4\u003c/sub\u003e] (-0.162\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.165 ppb). Thereafter, the simulations from 2020\u0026ndash;2022 indicated an \u003cem\u003eoverestimation\u003c/em\u003e of surface temperature (0.017\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.064\u0026ordm;C) and \u003cem\u003eunderestimations\u003c/em\u003e of total precipitation (-0.102\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.001 mm day\u003csup\u003e-1\u003c/sup\u003e. From 2020\u0026ndash;2022, this overall upward-trending pattern and potential overestimation of global mean surface temperature and total precipitation variability was analyzed with temporal shifts over the three-year period. From 2020\u0026ndash;2021, global weighted mean variability differencing of surface temperature and total precipitation from \u003cem\u003eG6Sulfur\u003c/em\u003e simulations and ERA5 was \u003cem\u003eoverestimated\u003c/em\u003e (1.981\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.031\u0026ordm;C, 0.855\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.004 mm day\u003csup\u003e-1\u003c/sup\u003e) while similarly, 2021\u0026ndash;2022 simulations \u003cem\u003eoverestimated\u003c/em\u003e observations, i.e., differencing resulted in surface temperature and total precipitation mean variability of 2.030\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.080\u0026ordm;C and 0.881\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.030 mm day\u003csup\u003e-1\u003c/sup\u003e, respectively, yielding net \u003cem\u003eoverestimations\u003c/em\u003e in surface temperature and total precipitation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e2d. G7Cirrus\u003c/h2\u003e\n\u003cp\u003eEvaluating the radiative forcing impacts of cloud seeding on land-atmospheric interactions foretells a different story. For this experiment, simulations promote increases in rate of cirrus ice crystallization (i.e., \u003cem\u003eG7Cirrus\u003c/em\u003e) to mitigate high emission baseline forcing from SSP585 by 1 Wm\u003csup\u003e-2\u003c/sup\u003e, resulting in substantial \u003cem\u003eincreases\u003c/em\u003e in global weighted mean of surface temperature (2.028\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.567\u0026ordm;C; -0.216-7.716\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC), total precipitation (0.093\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.412 mm day\u003csup\u003e-1\u003c/sup\u003e; -3.936-3.986 mm day\u003csup\u003e-1\u003c/sup\u003e), and [CH\u003csub\u003e4\u003c/sub\u003e] (1.515\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.124 ppb; -4.515-7.573 ppb) from 2020\u0026ndash;2100 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Additionally, global mean departures indicated consistent \u003cem\u003ereduced\u003c/em\u003e variability of surface temperature (-4.089\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e3.321\u0026ordm;C), total precipitation (-0.184\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.741 mm day\u003csup\u003e-1\u003c/sup\u003e), and [CH\u003csub\u003e4\u003c/sub\u003e] (-4.422\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.780 ppb) under cloud seeding-induced radiative effects.\u003c/p\u003e\n\u003cp\u003eThese patterns are most notably illustrated across the Arctic, with \u003cem\u003eincreasing\u003c/em\u003e surface temperature via weighted mean differencing across Greenland and Baffin Island, Northern Scandinavia, British Columbia and the Canadian Rockies, Southern Kazakhstan and Mongolia, Northern Siberia (i.e., Northern Trans-Siberian extent from Yamalo-Nenets Autonomous Okrug to the Sakha Republic), and the foothills and rainforests east of the Andes Mountains (i.e., Northern Argentina, Paraguay, Bolivia, and Western Amazonas). Alternatively, a number of cool spots proliferate across the Arctic as well; namely, the Chukchi and Beaufort Sea, Hudson Bay, Svalbard, and parts of the Barents and Kara Sea. Total precipitation events appear localized to equatorial regions of the western Arabian Sea and the North Pacific Ocean, though increasing trends of total precipitation are illustrated along the western coast of Jalisco, Central Bahia in South America, Uruguay, Southern Malawi, and Brisbane. Alternatively, significant dry spells are depicted in the Indian Ocean south of the Bay of Bengal and west of North Sumatra, east of the Philippines (i.e., Guam) and southwestern Colombia. Strong [CH4] anomalies are displayed in the Nord-du-Qu\u0026eacute;bec region, along the coast of Terra Nova Bay and the Gerlache Inlet (Amery Ice Shelf), a substantial anomaly near the Ross Ice Shelf in the South Ocean, and finally, localized hot spots in western Kalimantan and North Sumatra. Notable cool spots emerge off the coast of Queen Maud Land (i.e., Utsteinen Nunatak, East Ongul Island).\u003c/p\u003e\n\u003cp\u003eDuring ERA5 and \u003cem\u003eG7Cirrus\u003c/em\u003e model intercomparisons of surface temperature and total precipitation from 2020\u0026ndash;2022 - and after climatology was removed and spatial averaging was applied to compute global mean - the simulations \u003cem\u003eoverestimated\u003c/em\u003e the weighted mean of surface temperature (0.015\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.318\u0026ordm;C; -1.997-2.122\u0026ordm;C) and \u003cem\u003eunderestimated\u003c/em\u003e total precipitation (-0.004\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.321 mm day\u003csup\u003e-1\u003c/sup\u003e; -4.267-3.357 mm day\u003csup\u003e-1\u003c/sup\u003e). Surface temperature demonstrated interannual volatility between simulations and reanalysis data but generalized to a net negative \u0026minus;\u0026thinsp;0.116\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC reduction from 2020\u0026ndash;2022. Total precipitation revealed similar yet inverted patterns of volatility. Namely, these patterns illustrated divergent temperatures yielding periods of higher total precipitation accuracy - with a generalized diverging net positive trend of 0.052 mm day\u003csup\u003e-1\u003c/sup\u003e (i.e., P\u0026thinsp;\u0026gt;\u0026thinsp;ET). Computing global weighted mean departures for these variables from 2020\u0026ndash;2022 indicated the model\u0026rsquo;s tendency to \u003cem\u003eunderestimate\u003c/em\u003e both surface temperature (-0.611\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.023\u0026ordm;C; -4.172-4.717\u0026ordm;C) and total precipitation (-0.102\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.001 mm day\u003csup\u003e-1\u003c/sup\u003e; -4.771-5.453 mm day\u003csup\u003e-1\u003c/sup\u003e) based on temporal bounding conditions in the interest of ERA5 intercomparison efforts. The historical relationship (i.e., 2020\u0026ndash;2022) among ERA5-derived and simulated surface temperature and total precipitation resulted in a global annual weighted mean differencing of 1.792\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.028\u0026ordm;C and 0.893\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.023 mm day\u003csup\u003e-1\u003c/sup\u003e. Furthermore, we spatially flattened and temporally differenced the \u0026lsquo;validation\u0026rsquo; datasets from 1990\u0026ndash;2022, 1950\u0026ndash;2022, and 2020\u0026ndash;2022 to generate time series plots in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e illustrating surface temperature and total precipitation variability over a 73-year time period. These plots illustrate how simulations drift from the observational record.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we examined various mitigation and geoengineering experiments to understand the potential impacts of these strategies and to identify the most feasible solutions to address global climate change. Some of these experiments included CO\u003csub\u003e2\u003c/sub\u003e 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 \u003cem\u003ebrief\u003c/em\u003e 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 multi-scale biogeochemical processes. Moreover, potential unintended consequences resulting from each of these geoengineering scenarios are important to highlight; in particular, surface temperature and total precipitation briefly reach - and exceed - stabilization thresholds during the contemporary period in many of these experiments, with rapid warming and increased precipitation bolstering an enhanced runaway greenhouse effect.\u003c/p\u003e\n\u003cp\u003eCollectively, intervention and mitigation simulations tended to \u003cem\u003eoverestimate\u003c/em\u003e the variability and magnitude of surface temperature and total precipitation, with substantial regional deviations and scenario-dependent estimation heterogeneity for [CH\u003csub\u003e4\u003c/sub\u003e]. This highlights the challenges to accurately quantify and project greenhouse gas 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 exact outcomes of different geoengineering strategies. This suggests that model uncertainty may promulgate through the projections but represent a near-to-observational record that is nonetheless 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 apply 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.\u003c/p\u003e\n\u003cp\u003eRetrospectively, global mean surface temperatures and total precipitation derived from ERA5 reanalysis data indicate steady \u003cem\u003eincreases\u003c/em\u003e of 1.374\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.481\u0026deg;C and 0.045\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.567 mm day\u003csup\u003e-1\u003c/sup\u003e respectively. However, significant regional variations were identified, with Greenland, Northern Siberia, the Horn of Africa, and the Antarctic coastline experiencing \u003cem\u003eelevated\u003c/em\u003e surface temperatures as high as 7.626\u0026deg;C, while other regions including Central Africa, Southern Australia, Eastern Brazil, and Northwestern Mexico observed notable \u003cem\u003ereductions\u003c/em\u003e in surface temperature (-2.378\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC). Similarly, regions including Southern Alaska (e.g., Chugach and Copper River), the northern coast of Norway, South America (e.g., Western Ecuador, Rio Grande do Sul, Uruguay), Southern India, Northwestern Indonesia, and Africa (e.g., Central Africa Republic, Democratic Republic of the Congo, Angola, South Africa) experienced \u003cem\u003eincreased\u003c/em\u003e total precipitation (1.170 mm day\u003csup\u003e-1\u003c/sup\u003e), while South America (e.g., Colombia, Bolivia, Amazonas) and East Africa (e.g., Sudan, Ethiopia) experienced \u003cem\u003ereductions\u003c/em\u003e in total precipitation (-1.195 mm day\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eThough imperfect in comparison to observational records, these mitigation and intervention simulation experiments capture large-scale Earth system dynamics well; however, some discrepancies do emerge. Utilizing global mean surface temperature and total precipitation as points of departure \u0026ndash; with the understanding that regional-to-localized dynamics still require more spatially explicit refinement in Earth System Model frameworks\u003csup\u003e49\u003c/sup\u003e - reanalysis and model intercomparisons act as a quality assessment and validation baseline to inform calibration updates (i.e., forcing observations, parameterization, drivers) and improve future simulations. Each model run exhibited unique responses to experiment-specific mitigation (\u003cstrong\u003e1\u003c/strong\u003e) or intervention (\u003cstrong\u003e2\u003c/strong\u003e) strategies; most importantly, no experimental \u0026lsquo;solution\u0026rsquo; 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.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eMitigation scenarios were simulated to demonstrate the potential of CO\u003csub\u003e2\u003c/sub\u003e removal strategies to substantially alter global climatologies in the future; in particular, initialization of the \u003cstrong\u003e1a\u003c/strong\u003e \u003cem\u003e1pctCO2-cdr\u003c/em\u003e experiment resulted in global mean climatologies demonstrating consistent \u003cem\u003eincreases\u003c/em\u003e in surface temperature, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] from 1990\u0026ndash;2149 (3.885\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e2.624\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, 0.174\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.781 mm day\u003csup\u003e-1\u003c/sup\u003e, 0.013\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.269 ppb). In contrast, surface temperature and total precipitation were \u003cem\u003ereduced\u003c/em\u003e while [CH\u003csub\u003e4\u003c/sub\u003e] \u003cem\u003eincreased\u003c/em\u003e from 1990\u0026ndash;2022 (-0.010\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.365\u0026ordm;C, -0.024\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.892 mm day\u003csup\u003e-1\u003c/sup\u003e, 0.018\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.251 ppb), suggesting an effective cooling and drying mechanism in the short-term yet largely \u003cem\u003eunderestimates\u003c/em\u003e the evolution of surface temperature and total precipitation (4.161\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.054\u0026ordm;C, 0.980\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.010 mm day\u003csup\u003e-1\u003c/sup\u003e). Critically, this experiment precipitated pronounced Arctic amplification, with surface temperature in excess of 13.078\u0026deg;C across the tundra and boreal ecotones. In addition, the \u003cstrong\u003e1b\u003c/strong\u003e \u003cem\u003eesm-1pct-brch-1000PgC\u003c/em\u003e experiment demonstrated a more balanced climate response with less pronounced \u003cem\u003eincreases\u003c/em\u003e in global surface temperature, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] from 1950\u0026ndash;2149 in comparison to \u003cstrong\u003e1a\u003c/strong\u003e i.e., 0.061\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.347\u0026ordm;C, 0.003\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.331 mm day\u003csup\u003e-1\u003c/sup\u003e, 0.001\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.169 ppb. However, relative to the entire simulation period, regional variabilities resulted in surface temperature \u003cem\u003ereductions\u003c/em\u003e for the 1990\u0026ndash;2022 simulation period, with marginal \u003cem\u003eincreases\u003c/em\u003e in total precipitation and [CH\u003csub\u003e4\u003c/sub\u003e] trajectories (-0.032\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.299\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, 0.001\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.330 mm day\u003csup\u003e-1\u003c/sup\u003e, 0.007\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.155 ppb). The \u003cstrong\u003e1b\u003c/strong\u003e experiment indicates that even under aggressive CO\u003csub\u003e2\u003c/sub\u003e removal and carbon management strategies, complex climate feedbacks remain unfettered\u003csup\u003e50\u003c/sup\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVarious 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 surface temperature, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] patterns with varying regional impacts. The \u003cstrong\u003e2a\u003c/strong\u003e \u003cem\u003eG1\u003c/em\u003e experiment (i.e., 4xCO\u003csub\u003e2\u003c/sub\u003e and solar dimming) illustrates global radiative balance by reducing solar irradiance to stabilize climatological feedbacks, resulting in significant surface temperature modulations (i.e., slight cooling), extensive variability in total precipitation, and \u003cem\u003eincreasing\u003c/em\u003e [CH\u003csub\u003e4\u003c/sub\u003e] across the simulated period, i.e., 1850\u0026ndash;1949 (-0.010\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.472\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, -0.010\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.321 mm day\u003csup\u003e-1\u003c/sup\u003e, 0.003\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.180 ppb). However, regional hotspots were distinguishable, with pronounced warming trends and emergent climatic patterns, trends, and anomalies. In the \u003cstrong\u003e2b\u003c/strong\u003e \u003cem\u003eG6Solar\u003c/em\u003e experiment, high-to-medium solar forcing reduction promoted cooling trends, with elevated temperature and [CH\u003csub\u003e4\u003c/sub\u003e] hotspots despite an overall reduction in global mean variability (i.e., departures). Despite reducing surface temperatures, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] from 2020\u0026ndash;2022 (-2.545\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e2.605\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC, -0.083\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.612 mm day\u003csup\u003e-1\u003c/sup\u003e, -4.368\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.623 ppb) \u0026ndash; and cultivating cool, dry spots across the globe in the short-term \u0026ndash; surface temperatures, total precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] rebounded and increased over the 2020\u0026ndash;2100 simulation period (1.275\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.262\u0026ordm;C, 0.045\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.347 mm day\u003csup\u003e-1\u003c/sup\u003e, 1.575\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e1.075 ppb). The \u003cstrong\u003e2c\u003c/strong\u003e experiment (\u003cem\u003eG6Sulfur\u003c/em\u003e) 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 precipitation departures (i.e., variability) by doing so. Additionally, the results suggest this strategy may present abrupt climatic changes and pose significant long-term stability issues beyond initial beneficial impacts are achieved and aerosol effects are diminished. Similarly, the \u003cstrong\u003e2d\u003c/strong\u003e experiment (\u003cem\u003eG7Cirrus\u003c/em\u003e) facilitated an increase in surface temperature and [CH4] initially, though regional patterns of reduced temperature variability were observed. In the long term, the impacts on precipitation variability are significant. Collectively, these results suggest a complex interplay of oscillating dynamics between global, regional, and localized climate regimes and highlight the challenges to forecast accurate outcomes under mitigation and intervention mechanisms.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe extended the distribution curve tails toward a \u0026lsquo;new normal\u0026rsquo; by increasing CO\u003csub\u003e2\u003c/sub\u003e, seemingly magnifying contemporary observed changes of punctuated extreme weather and precipitation\u003csup\u003e51\u003c/sup\u003e. This variability introduces shifts in regional climates, though it may also be responsible for the increased warming trend across the globe. While the range of temperature and precipitation in \u003cstrong\u003e1b\u003c/strong\u003e is less \u003cstrong\u003e1a\u003c/strong\u003e, precipitation is increasingly variable in the models, with interannual shifts in precipitation 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 precipitation, governed by biogeochemical processes that are not fully captured by the models, is of concern. Moreover, by employing and extending the \u003cstrong\u003e1a\u003c/strong\u003e experiment beyond the 1000PgC benchmark, this facilitates increasing loss of atmospheric carbon [CH\u003csub\u003e4\u003c/sub\u003e] beyond net zero that may have biogeochemical consequences over time. Regional incongruencies in temperature and precipitation are localized to hot and cool dry ecotones exhibiting high net primary productivity and the persistent conversion from carbon sinks to sources.\u003c/p\u003e\n\u003cp\u003eThese \u003cstrong\u003e2b\u003c/strong\u003e simulation outputs of global weighted mean variabilities indicate synergistic co-regulated feedbacks, with persistent trends characterized by abrupt pulses; specifically, [CH\u003csub\u003e4\u003c/sub\u003e] began and ended the simulation period with net emissions in terms of annual magnitude, but throughout most of the simulation period, [CH\u003csub\u003e4\u003c/sub\u003e] variability decreased and remained low. In contrast, total precipitation variability illustrated anticorrelated behavior, i.e., began and ended the simulation period with minimal precipitation events and accumulation but rather a steady increase followed by a gradual decrease \u0026ndash; in enhanced total precipitation variability. Surface temperature characteristically stimulates climate feedback patterns governing water vapor distribution and precipitable accumulation. During this validation window, surface temperature and total precipitation differencing and dimensionality reduction via spatial flattening demonstrates that over regions of high variability of total precipitation, surface temperature departures were marginal, remaining stabilized (i.e., oceans, equatorial tropics); however, less precipitation variability prompted an elevation of surface temperature variability, triggering positive climate feedbacks (i.e., high latitudes). These trends remain periodic with gradual increases in surface temperature and total precipitation \u0026ndash; in concert with net release yield from [CH\u003csub\u003e4\u003c/sub\u003e] \u0026ndash; through the end of the century, followed by a slight stabilization period.\u003c/p\u003e\n\u003cp\u003eMarked reductions in global mean departures from \u003cstrong\u003e2c\u003c/strong\u003e simulations suggest the employment of this strategy will indeed cool the planet while synergistically amplifying the positive coupling feedbacks between temperature and precipitation, 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 total precipitation variability across the entirety of Eastern Brazil \u0026ndash; from Bahia to S\u0026atilde;o Paulo - as well as surface temperature and [CH\u003csub\u003e4\u003c/sub\u003e] 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.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, 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, when examining these figures, it is important to note that global mean variability between the two experiments from 2020\u0026ndash;2100 results in strikingly different outcomes, thus illustrating how climatologies change, anomalies evolve, and global climate change is fostered by land-ocean-atmospheric interactions over space and time.\u003c/p\u003e\n\u003cp\u003eSurface temperature variability in \u003cstrong\u003e2c\u003c/strong\u003e is likely coupled with unique surface flux exchanges and biogeophysical dynamics, though it is more likely coupled with the erratic variability of total precipitation. SAI has the potential to mitigate solar irradiance within Earth\u0026rsquo;s atmosphere- for a finite amount of time. The model results respond accordingly, with steady, linear, and increases in temperature and precipitation for the first 30 years after initialization, followed by an abrupt jump in global mean temperature. These dynamics may be caused by feedback interactions with the Earth systems or due to a loss of atmospheric sulfate over time, but the abating of temperatures allowing for a stabilized system without fossil fuel reductions is of concern, when followed by abrupt increases. This strategy is less variable on the year-to-year scale but presents problems for long-term climate stability. Relative to baseline simulations, SAI methods facilitate similar trends to these patterns. However, the magnitude of global weighted mean variability of total precipitation is significantly higher as a consequence of \u003cstrong\u003e2b\u003c/strong\u003e, while surface temperature exhibits lower variability. In addition, \u003cstrong\u003e2a\u003c/strong\u003e and \u003cstrong\u003e2b\u003c/strong\u003e produced anticorrelated covariate trends, with \u003cstrong\u003e2a\u003c/strong\u003e showing higher variability of surface temperature, precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e]. This is due to solar irradiance reduction techniques. The \u003cstrong\u003e2a\u003c/strong\u003e 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 very different approaches seek to address and resolve uncertainty challenges by altering light or matter.\u003c/p\u003e\n\u003cp\u003eAnother example of the anticorrelated covariate trending is \u003cstrong\u003e2c\u003c/strong\u003e and \u003cstrong\u003e1a\u003c/strong\u003e. In this case, \u003cstrong\u003e2c\u003c/strong\u003e induces a reduction in surface temperature and atmospheric methane variability while increasing total precipitation variability on a global scale. In contrast, \u003cstrong\u003e1a\u003c/strong\u003e increases surface temperature variability coupled with a less variable global weighted mean in total precipitation. The variability of global weighted mean of [CH\u003csub\u003e4\u003c/sub\u003e] was significant during \u003cstrong\u003e2c\u003c/strong\u003e, representing nearly four orders of variable magnitude relative to [CH\u003csub\u003e4\u003c/sub\u003e] output variability from \u003cstrong\u003e1a\u003c/strong\u003e. The dissimilar system responses reiterate that small perturbations can have significant effects when magnified across space and time. During the \u003cstrong\u003e2d\u003c/strong\u003e experiment, the range of mean temperature is reduced considerably, together with surface temperature. The elevation in precipitation may be marginal but the variance has a long tail towards 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.\u003c/p\u003e\n\u003cp\u003eClimate change remains a critical issue, and the likelihood of geoengineering intervention in the future is nearly\u003c/p\u003e\n\u003cp\u003ecertain. 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 not yet achieved the necessary deployment scale required for climate change mitigation, and their impacts on the Earth system remain poorly understood. Through detailed simulation analyses, this research study \u0026ndash; presuming other approaches to this challenge are generalized and not comprehensive \u0026ndash; identified substantial variability in global, regional, and localized climate patterns from ERA5 reanalysis observations over a period of 73 years (i.e., 1950\u0026ndash;2022) as well as six mitigation and intervention simulations from CMIP6 experiments (i.e., CDRMIP, GeoMIP) from 1850\u0026ndash;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.\u003c/p\u003e\n\u003cp\u003eThe potential contributions and sources of error resulting in simulation over- and under-estimations may originate from computational methods, statistical liberties (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 to the system with restriction and potential information loss from scaling efforts. To reduce potential error and uncertainty originating from coarsely resolved scaling practices, future efforts will incorporate a multi-model multi-variant ensemble (i.e., UKESM1-0-LL, CanESM5, CNRM-ESM2-1, MIROC-ES2L, MPI-ESM1-2-LR). Though stability and consistency are important, the ability to validate historical simulations remains a challenge. To bolster validation efforts pre-1950, we will introduce cutting-edge methodologies, including artificial intelligence (AI) frameworks and optimization (AIO) to better quantify and understand dynamics, patterns, and trends that emerge over time. Furthermore, this adoption will enable knowledge discovery and confidence for recommendation systems and contingency formulation. AI methods seem poised to 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.\u003c/p\u003e\n\u003cp\u003eAI 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 tractability\u003csup\u003e52\u003c/sup\u003e. The integration of AI into geoengineering playbooks presents inherent risks that are not yet fully understood\u003csup\u003e40\u003c/sup\u003e. 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. Coupled with the dearth of uncertainty quantification and transparency, AIO strategies help address knowledge gaps and improve model performance. 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 on global climate change.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe utilized ERA5 reanalysis data as well as mitigation (i.e., CDRMIP) and intervention (i.e., GeoMIP) simulations to first examine baseline conditions to gain a \u003cem\u003eretrospective\u003c/em\u003e understanding of climate warming \u003cem\u003ethen\u003c/em\u003e prognosticating climate response patterns under a variety of mitigation and climate engineering scenarios. We compared observation-based ERA5 reanalysis data with simulation outputs from the CDRMIP and GeoMIP experiments. The temporal bounds of the simulation experiments were constrained to the observational period (i.e., 1950-2022) for surface temperature and total precipitation intercomparisons (i.e., \u003cem\u003e1pctCO2-cdr\u003c/em\u003e), 1950-2022 (i.e., \u003cem\u003eesm-1pct-brch-1000PgC\u003c/em\u003e), and 2020-2022 (i.e., \u003cem\u003eG6Solar\u003c/em\u003e; \u003cem\u003eG6Sulfur\u003c/em\u003e; \u003cem\u003eG7Cirrus\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eThe UKESM1-0-LL model (i.e., r1i1p1f2 ensemble member) was selected for this study because it is one of only six projects involved with both mitigation and climate engineering efforts (i.e., CDRMIP, GeoMIP) while offering a breadth of ensemble variants and surface variables at various temporal resolutions on a Native N96 grid. For sensitivity analyses, precipitation flux simulations derived from these simulations was converted to total precipitation (i.e., kg m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e to mm day\u003csup\u003e-1\u003c/sup\u003e) based on temporal windowing and the global surface basin. In addition, the sub-tropospheric multilevel mean of [CH\u003csub\u003e4\u003c/sub\u003e] was converted to parts per billion (i.e., mol mol\u003csup\u003e-1\u003c/sup\u003e to ppb).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eERA5 Reanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eERA5 Reanalysis observations (e.g., 2-m temperature, total precipitation) were regridded from (721, 1440) at 0.25\u0026nbsp;resolution with 3.88B samples to (143, 191) at 2.348\u0026nbsp;resolution with 16.03M observations (i.e., 27320 global observations per annum). The standard reference period as defined by the WMO identifies climate normal (i.e., 1991-2020); however, ERA5 Reanalysis data utilizes 1981-2010 as a base period, and therefore, we selected this time period, i.e., 1981-2010. ERA5 monthly averaged high-dimensional reanalysis datasets were extracted from the European Centre for Medium-Range Weather Forecasts \u003ca href=\"https://cds.climate.copernicus.eu/\"\u003eClimate Data Store\u003c/a\u003e portal generated by Copernicus Climate Change Service. The CDS API tool was utilized to efficiently query the database with appropriate parameters in place, i.e., global coverage over a 73-year period. Following regridding and scaling, these datasets were loaded, concatenated and restructured into a dataframe.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCMIP6 Simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition, 699.72M modeling outputs including abiotic measurements, e.g., surface temperature, precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e] derived from CMIP6 projects including the Geoengineering Model Intercomparison Project (i.e., GeoMIP) and the Carbon Dioxide Removal Model Intercomparison Project (i.e., CDRMIP) experiments (i.e., concluding NaN dropping via bilinear interpolation, backfilling, and forward-filling, 341.40M and 358.32M, respectively). Subsets of these experiments were searched and downloaded from the LLNL metagrid node with the ESGF PyClient API.\u003c/p\u003e\n\u003cp\u003eNumerous geoengineering and CO\u003csub\u003e2\u003c/sub\u003e removal intercomparison scenarios adopt sophisticated architectures and assessment protocols (e.g., CDRMIP, GeoMIP). However, mitigation efforts are limited in scope and often formulate uninformed strategies based on single high-emissions pathways inconsistent with near-term projections. The CDRMIP project aims to consolidate Earth system models within a unified framework to evaluate the feasibility, effects, and challenges associated with CDR technologies. The GeoMIP project aims to meet this research need by standardizing experiments across participating climate models. This initiative will enable identification of both commonalities and discrepancies of climate response to mitigation and intervention in model predictions, thereby contributing vital insights into potential outcomes of significant global efforts.\u003c/p\u003e\n\u003cp\u003eWe examined mitigation and intervention strategies employed by assessment protocol and SSP scenarios to identify global historical and future relationships and climate change indicators from 1850-2149 (e.g., surface temperature, precipitation, and [CH\u003csub\u003e4\u003c/sub\u003e]). Due to escalating extreme events and more support growing for weather modification tactics, it was necessary to not only examine anomalies, trends, and bounds of the datasets but to also constrain the temporal look-forward period to 2050 in the interest of mitigation and policy implementation. We examined these experimental runs from 1850 to 2149, with careful scrutiny given to periods of data misalignment and natural disturbance events (e.g., Pinatubo), and further examination during the years 2020 and 2050, notating critical information that may prove useful in terms of deployment decision-making.\u003c/p\u003e\n\u003cp\u003eA more comprehensive list of results distributed by experiment, period, covariate, and uncertainty is provided in \u003cstrong\u003eS1\u003c/strong\u003e. GeoMIP simulations were analyzed to understand the historical and future implications of current strategies and the potential consequences of implementing geoengineering practices. Baselines were established to provide historical lenses (i.e., G1) and real-world atmospheric forcing (i.e., ERA5) to determine feasibility of contemporary geoengineering proposals and the associated impacts on the earth system over time. Concluding initial baseline runs, we extended the temporal retrospection period by 100 years with historical simulations (i.e., 1850-1949, \u003cem\u003eG1\u003c/em\u003e) and the prognostication period by 77-128 years with forward projections of intervention (i.e., 2023-2100, \u003cem\u003eG6Solar\u003c/em\u003e, \u003cem\u003eG6Sulfur\u003c/em\u003e, \u003cem\u003eG7Cirrus\u003c/em\u003e) and mitigation experiments (i.e., 2023-2149, \u003cem\u003e1pctCO2-cdr\u003c/em\u003e, \u003cem\u003eesm-1pct-brch-1000PgC\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese data products were loaded, appended, scaled, and reframed in each experiment-specific sequencing of the dataframe (i.e., CDRMIP: (199065600, 5)\u003csup\u003e1PCT\u003c/sup\u003e), GeoMIP: (80621568, 5)\u003csup\u003eG1\u003c/sup\u003e, (159252480, 5)\u003csup\u003eGSSC\u003c/sup\u003e) prior to assimilating with observation-derived ERA5 data with a bilinear periodic regridding algorithm (i.e., xESMF Regridder) to improve performance, compatibility, and interpretability. Because the CMIP6 experiments yielded daily outputs, monthly mean resampling was conducted across these simulation datasets to align with ERA-Land reanalysis monthly observations.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eERA5 global monthly averaged observations (e.g., 2-meter temperature, total precipitation): 24.25M regridded data points from 1950-2022\u003c/li\u003e\n\u003cli\u003eCMIP6 CDRMIP and GeoMIP Experiments (e.g., surface temperature, precipitation flux, [CH\u003csub\u003e4\u003c/sub\u003e]): 16.03M data points from 1850-2149\n\u003cul\u003e\n\u003cli\u003e\u003cem\u003e1pctCO2-cdr\u003c/em\u003e: In wake of 4xCO\u003csub\u003e2\u003c/sub\u003e preindustrial baseline, one percent CO\u003csub\u003e2\u003c/sub\u003e reduction per year is prescribed until preindustrial control is reached and maintained (1990-2149)\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eesm-1pct-brch-1000PgC\u003c/em\u003e: After 1000Pg cumulative emissions threshold achieved, zero emissions are simulated after \u003cem\u003e1pctCO2-cdr\u003c/em\u003e run (1950-2149)\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eG1\u003c/em\u003e: 4xCO\u003csub\u003e2\u003c/sub\u003e mitigation via solar radiation management (1850-1949)\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eG6Solar\u003c/em\u003e: High forcing scenario reduction to medium forcing via solar radiation management, i.e.,\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003esolar irradiance reduction (2020-2100)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cem\u003eG6Sulfur\u003c/em\u003e: High-to-medium forcing scenario reduction; sulfate aerosol injection, SAI (2020-2100)\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eG7Cirrus\u003c/em\u003e: High forcing scenario baseline mitigation via increases in rate and magnitude of cirrus ice\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ecrystallization, i.e., cloud seeding (2020-2100)\u003c/p\u003e\n\u003cp\u003eThese dataframes were concatenated along the temporal dimension, resulting in a (23951989, 93) dataframe with 93 variables obtained from ERA5 reanalysis observations across 1950-2022 (i.e., 2-meter temperature, total precipitation), CDRMIP \u003cem\u003e1pctCO2-cdr\u003c/em\u003e and \u003cem\u003eesm-1pct-brch-1000PgC\u003c/em\u003e experiments from 1950-2022 and 1990-2022 respectively (i.e., surface temperature, precipitation flux, [CH\u003csub\u003e4\u003c/sub\u003e]), GeoMIP \u003cem\u003eG1\u003c/em\u003e spin-up simulations from 1850-1949 (i.e., surface temperature, precipitation flux, [CH\u003csub\u003e4\u003c/sub\u003e]), and GeoMIP \u003cem\u003eG6\u003c/em\u003e and \u003cem\u003eG7\u003c/em\u003e geoengineering experiments from 1850-2100, and radiatively forced OSCAR v3.3 modeling outputs across 1750-2022. Beyond 2022, G6 and G7 experiments (2023-2149) and OSCAR v3.3 modeling output (2023-2100) serve as proxy for testing the prediction abilities of the model (i.e., seasonal, annual, decadal, semicentennial) while accounting for model drift anomalies that could propagate in space and time. In addition, employing this data for testing helps identify key temporal relationships that may provide more comprehensive understanding of local-to-regional effects of disaggregation and down-sampling methods to further disentangle anomaly detection (i.e., localizing trend and/or feedback dynamics that require data-informed scenario-based decision-making. Various scenarios in place.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe ideas and views conceived and conveyed within this article are the authors\u0026rsquo; own and do not represent the opinion or policy of the National Aeronautics and Space Administration, Jet Propulsion Laboratory, or California Institute of Technology. BG\u0026rsquo;s participation was supported by an appointment to the NASA Postdoctoral Program at the Jet Propulsion Laboratory, California Institute of Technology, administered by Oak Ridge Associated Universities. NASA Jet Propulsion Laboratory operates under a contract with the National Aeronautics and Space Administration (80NM0018D0004). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Aeronautics and Space Administration (NASA) or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.\u003c/p\u003e\n\u003ch2\u003eAuthor\u0026nbsp;contributions\u003c/h2\u003e\n\u003cp\u003eB.G. and C.M. envisioned the original research question and B.G. managed the analyses and organized the drafting process. L.M. and K.M. reviewed and edited drafts, and C.M. edited and contributed text. All authors reviewed the manuscript. The following elaborates on the roles and contributions of the authors in accordance with CrediT principles: Conceptualization: B.G., L.M., K.M., C.M.; Methodology: B.G.; Investigation: B.G..; Visualization: B.G.; Funding Acquisition: L.M., K.M., C.M.; Project Administration: L.M., K.M., C.M.; Supervision: C.M.; Writing - Original Draft: B.G., L.M., K.M., C.M.; Writing - Review \u0026amp; Editing: B.G., K.M., C.M.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eA comprehensive list of the datasets and code supporting the findings of this study is available in the Data Citations subsection below and openly accessible through the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Additional long-form background and methods are provided in the Supporting Information and in wiki format. The Jupyter Notebook used for pre-processing, synthesis, and analysis in the paper alongside an executable version in the cloud hosted on GitHub is made available at the links below. The codebase will be preserved at the JPL Open Repository (JOR). DOI: TBD\u003c/p\u003e\n\u003cp\u003ehttps://github.com/bradleygay/geoengai/wiki\u003c/p\u003e\n\u003cp\u003ehttps://github.com/bradleygay/geoengai/tree/master\u003c/p\u003e\n\u003cp\u003ehttps://obscure-bassoon-547gxwvj4p42pvv4.github.dev/?editor=jupyter\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDatasets\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eRepositories, data archives, simulation results, and synthesis work are publicly available (GitHub, JPL DOI). Much of the state, diagnostic, and prognostic variables are inferred and/or derived from global annual mean mixing ratios, harmonized emissions, global annual mean radiative forcing, SSP-based simulations and scenario projections (i.e., SSPs), and subsequent geoengineering experiments (e.g., GeoMIP, CDRMIP). The corresponding models, variables, and frequency are enumerated in the Supplementary Information.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eActivity Nodes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCMIP5 Scenarios (RCPs): https://www.pik-potsdam.de/~mmalte/rcps\u003c/p\u003e\n\u003cp\u003eCMIP6 Scenarios (SSPs): ScenarioMIP: https://aims2.llnl.gov/search\u003c/p\u003e\n\u003cp\u003eCMIP6 Experiments (MIPs): CDRMIP, GeoMIP: https://aims2.llnl.gov/search\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModels\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eScenarioMIP (59 Models)\u003c/p\u003e\n\u003cp\u003eACCESS-CM2, ACCESS-ESM1-5, AWI-CM-1-1-MR, BCC-CSM2-MR, CAMS-CSM1-0, CAS-ESM2-0, CESM2, CESM2-FV2, CESM2-WACCM, CIESM, CMCC-CM2-SR5, CMCC-ESM2, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, CanESM5, CanESM5-1, CanESM5-CanOE, E3SM-1-0, E3SM-1-1,E3SM-1-1-ECA, E3SM-2-0, EC-Earth3, EC-Earth3-AerChem, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR, FGOALS-f3-L, FGOALS-g3, FIO-ESM-2-0, GFDL-CM4, GFDL-ESM4, GISS-E2-1-G, GISS-E2-1-G-CC, GISS-E2-1-H, GISS-E2-2-G, HadGEM3-GC31-LL, HadGEM3-GC31-MM, IITM-ESM, INM-CM4-8, INM-CM5-0, IPSL-CM5A2-INCA, IPSL-CM6A-LR, KACE-1-0-G, KIOST-ESM, MCM-UA-1-0, MIROC-ES2H, MIROC-ES2L, MIROC6, MPI-ESM-1-2-HAM, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NESM3, NorESM2-LM, NorESM2-MM, TaiESM1,\u0026nbsp;\u003cstrong\u003eUKESM1-0-LL\u003c/strong\u003e, UKESM1-1-LL\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/ScenarioMIP/\"\u003ehttps://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/ScenarioMIP/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCDRMIP (12 Models)\u003c/p\u003e\n\u003cp\u003eACCESS-ESM1-5, CanESM5, CanESM5-CanOE, CAS-ESM2-0, CESM2, CNRM-ESM2-1, GFDL-ESM4, GISS-E2-1-G-CC, MIROC-ES2L, MPI-ESM1-2-LR, NorESM2-LM, \u003cstrong\u003eUKESM1-0-LL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/CDRMIP/\"\u003ehttps://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/CDRMIP/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGeoMIP (8 Models)\u003c/p\u003e\n\u003cp\u003eCanESM5, CESM2-WACCM, CNRM-ESM2-1, IPSL-CM6A-LR, MIROC-ES2H, MPI-ESM1-2-HR, MPI-ESM1-2-LR, \u003cstrong\u003eUKESM1-0-LL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/GeoMIP/\"\u003ehttps://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/GeoMIP/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCDRMIP Experiments | UKESM1-0-LL r1i1p1f2 member\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e1pctCO2-cdr\u003c/p\u003e\n\u003cp\u003e1 percent per year decrease in CO\u003csub\u003e2\u003c/sub\u003e from 4xCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://doi.org/10.22033/ESGF/CMIP6.12183\"\u003ehttp://doi.org/10.22033/ESGF/CMIP6.12183\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ets: CMIP6.CDRMIP.MOHC.UKESM1-0-LL.1pctCO2-cdr.r1i1p1f2.Amon.ts.gn.v20200425|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003epr: CMIP6.CDRMIP.MOHC.UKESM1-0-LL.1pctCO2-cdr.r1i1p1f2.Amon.pr.gn.v20200425|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003e[CH\u003csub\u003e4\u003c/sub\u003e]: CMIP6.CDRMIP.MOHC.UKESM1-0-LL.1pctCO2-cdr.r1i1p1f2.Amon.ch4.gn.v20200425|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003eesm-1pct-brch-1000PgC\u003c/p\u003e\n\u003cp\u003eZero emissions simulation branched from 1percent run after 1000 PgC cumulative emission\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://doi.org/10.22033/ESGF/CMIP6.10785\"\u003ehttp://doi.org/10.22033/ESGF/CMIP6.10785\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ets: CMIP6.C4MIP.MOHC.UKESM1-0-LL.esm-1pct-brch-1000PgC.r1i1p1f2.Amon.ts.gn.v20200210|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003epr: CMIP6.C4MIP.MOHC.UKESM1-0-LL.esm-1pct-brch-1000PgC.r1i1p1f2.Amon.pr.gn.v20200210|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003e[CH\u003csub\u003e4\u003c/sub\u003e]: CMIP6.C4MIP.MOHC.UKESM1-0-LL.esm-1pct-brch-1000PgC.r1i1p1f2.Amon.ch4.gn.v20200210|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGeoMIP Experiments | UKESM1-0-LL r1i1p1f2 ensemble member\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eG1\u003c/p\u003e\n\u003cp\u003eAbrupt quadrupling of CO\u003csub\u003e2\u003c/sub\u003e plus reduction in total solar irradiance\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://doi.org/10.22033/ESGF/CMIP6.5812\"\u003ehttp://doi.org/10.22033/ESGF/CMIP6.5812\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ets: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G1.r1i1p1f2.Amon.ts.gn.v20190916|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003epr: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G1.r1i1p1f2.Amon.pr.gn.v20190916|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003e[CH\u003csub\u003e4\u003c/sub\u003e]: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G1.r1i1p1f2.Amon.ch4.gn.v20190916|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003eG6Solar\u003c/p\u003e\n\u003cp\u003eTotal solar irradiance reduction to reduce net forcing from SSP585 to SSP245\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://doi.org/10.22033/ESGF/CMIP6.5820\"\u003ehttp://doi.org/10.22033/ESGF/CMIP6.5820\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ets: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6solar.r1i1p1f2.Amon.ts.gn.v20191031|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003epr: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6solar.r1i1p1f2.Amon.pr.gn.v20191031|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003e[CH\u003csub\u003e4\u003c/sub\u003e]: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6solar.r1i1p1f2.Amon.ch4.gn.v20191031|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003eG6Sulfur\u003c/p\u003e\n\u003cp\u003eStratospheric sulfate aerosol injection to reduce net forcing from SSP585 to SSP245\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://doi.org/10.22033/ESGF/CMIP6.5822\"\u003ehttp://doi.org/10.22033/ESGF/CMIP6.5822\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ets: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6sulfur.r1i1p1f2.Amon.ts.gn.v20191113|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003epr: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6sulfur.r1i1p1f2.Amon.pr.gn.v20191113|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003e[CH\u003csub\u003e4\u003c/sub\u003e]: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G6sulfur.r1i1p1f2.Amon.ch4.gn.v20191113|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003eG7Cirrus\u003c/p\u003e\n\u003cp\u003eIncrease cirrus ice crystal fall speed to reduce net forcing in SSP585 by 1 W m-2\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://doi.org/10.22033/ESGF/CMIP6.5828\"\u003ehttp://doi.org/10.22033/ESGF/CMIP6.5828\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ets: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G7cirrus.r1i1p1f2.Amon.ts.gn.v20191125|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003epr: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G7cirrus.r1i1p1f2.Amon.pr.gn.v20191125|esgf.ceda.ac.uk\u003c/p\u003e\n\u003cp\u003e[CH\u003csub\u003e4\u003c/sub\u003e]: CMIP6.GeoMIP.MOHC.UKESM1-0-LL.G7cirrus.r1i1p1f2.Amon.ch4.gn.v20191125|esgf.ceda.ac.uk\u003c/p\u003e\n\u003ch2\u003eAdditional\u0026nbsp;information\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare this research was conducted without competing interests, commercial, and/or financial relationships that may be construed as a potential conflict of interest. All data and codebase are contained in the text, supplementary information, and publicly accessible in the interest of open access, transparency and reproducibility.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLenton, T.M., \u0026amp; Vaughan, N.E. (2009). The radiative forcing potential of different climate geoengineering options. \u003cem\u003eAtmospheric Chemistry and Physics 9\u003c/em\u003e(15), 5539\u0026ndash;5561. https://doi.org/10.5194/acp-9-5539-2009\u003c/li\u003e\n\u003cli\u003eSchwalm, C.R., Glendon, S. and Duffy, P.B., 2020. RCP8. 5 tracks cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e(33), pp.19656\u0026ndash;19657. https://doi.org/10.1073/pnas.2007117117\u003c/li\u003e\n\u003cli\u003eSchimel, D., \u0026amp; Miller, C. (2023). 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A literature review on combining heuristics and exact algorithms in combinatorial optimization. \u003cem\u003eEuropean Journal of Information Technologies and Computer Science\u003c/em\u003e, \u003cspan class=\"BoldItalic\"\u003e2\u003c/span\u003e(2), 6\u0026ndash;12. https://doi.org/10.24018/ejcompute.2022.2.2.50\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"geoengineering, climate change, carbon cycle, CMIP6, Earth Observations, Earth system model","lastPublishedDoi":"10.21203/rs.3.rs-4469037/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4469037/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGiven a world increasingly dominated by climate extremes, large-scale geoengineering interventions to modify the Earth\u0026rsquo;s climate appears inevitable. However, geoengineering faces a conundrum: accurately forecasting the consequences of climate intervention in a system for which we have incomplete observations and an imperfect understanding. We evaluate the potential implications of mitigation and intervention strategies with a set of experiments utilizing historical reanalysis data and scenario-based model simulations to examine the global response to deploying these strategies. Key findings included a global mean surface temperature and total precipitation increases of 1.374\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.481\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC and 0.045\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.567 mm day\u003csup\u003e\u0026minus;1\u003c/sup\u003e respectively over the observed period (i.e., 1950\u0026ndash;2022). Mitigation and intervention simulations reveal pronounced regional anomalies in surface temperature and erratic interannual variability in total precipitation, with surface temperatures up to 7.626\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eC in Greenland, Northern Siberia, and the Horn of Africa down to -2.378\u0026ordm;C in Central Africa and Eastern Brazil, and total precipitation increases of 1.170 mm day\u003csup\u003e\u0026minus;1\u003c/sup\u003e in Southern Alaska down to -1.195 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in Colombia and East Africa. Furthermore, [CH\u003csub\u003e4\u003c/sub\u003e] dynamics indicated the potential to alter global and regional climate metrics but presented significant regional and global variability based on scenario deployment. Collectively, intervention and mitigation simulations tended to \u003cem\u003eoverestimate\u003c/em\u003e the variability and magnitude of surface temperature and total precipitation, with substantial regional deviations and scenario-dependent estimation heterogeneity for [CH\u003csub\u003e4\u003c/sub\u003e]. Furthermore, forward projections indicate that both mitigation and intervention scenarios can lead to varied climate responses, emphasizing the complexity and uncertainty in predicting exact outcomes of different geoengineering strategies. By constraining our investigation scope to include monthly surface temperature, total precipitation, and atmospheric methane concentration [CH\u003csub\u003e4\u003c/sub\u003e], we find these simulations were capable of accurately capturing departures but unable to perfectly represent patterns of warming and precipitation teleconnections clearly identified in the observational record.\u003c/p\u003e","manuscriptTitle":"Assessing Earth System Responses to Climate Mitigation and Intervention with Scenario-Based Simulations and Data-Driven Insight","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 07:51:49","doi":"10.21203/rs.3.rs-4469037/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-21T06:33:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-19T01:15:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-01T10:17:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153990669280547888759559030738715051080","date":"2024-10-24T07:25:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201180437460682356718600996269728733795","date":"2024-10-22T09:46:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-09T03:50:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-05T19:50:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-25T14:33:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-24T13:44:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-23T21:34:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a65f0765-0bc4-4190-a9ff-b12232718c9d","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32446166,"name":"Earth and environmental sciences/Climate sciences"},{"id":32446167,"name":"Earth and environmental sciences/Natural hazards"},{"id":32446168,"name":"Physical sciences/Mathematics and computing"},{"id":32446169,"name":"Physical sciences/Energy science and technology"}],"tags":[],"updatedAt":"2025-03-10T19:47:44+00:00","versionOfRecord":{"articleIdentity":"rs-4469037","link":"https://doi.org/10.1038/s41598-025-91195-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-09 15:56:55","publishedOnDateReadable":"March 9th, 2025"},"versionCreatedAt":"2024-06-06 07:51:49","video":"","vorDoi":"10.1038/s41598-025-91195-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-91195-6","workflowStages":[]},"version":"v1","identity":"rs-4469037","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4469037","identity":"rs-4469037","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}