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The last decade has seen broad exploratory research into stratospheric aerosol (SA) geoengineering, motivated by concern that reducing greenhouse gas emissions may be insufficient to avoid significant impacts from climate change. Based on this research, it is plausible that a limited deployment of SA geoengineering, provided it is used in addition to cutting emissions, could reduce many climate risks for most people. However, “plausible” is an insufficient basis on which to support future decisions. Developing the necessary knowledge requires a transition toward mission-driven research that has the explicit goal of supporting informed decisions. We highlight two important observations that follow from considering such a comprehensive, prioritized natural-science research effort. First, while field experiments may eventually be needed to reduce some of the uncertainties, we expect that the next phase of research will continue to be primarily model-based, with one outcome being to assess and prioritize which uncertainties need to be reduced (and, as a corollary, which field experiments can reduce those uncertainties). Second, we anticipate a clear separation in scale and character between small-scale experimental research to resolve specific process uncertainties and global-scale activities. We argue that the latter, even if the radiative forcing is negligible, should more appropriately be considered after a decision regarding whether and how to deploy SA geoengineering, rather than within the scope of “research” activities.

Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low‐cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low‐cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio‐temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity. Plain Language Summary Ideally, to study how damaging phenomena like heatwaves, droughts and downpours will change in the future under global warming, we would want a large number of climate model runs producing many realizations of climate futures that we can analyze and from which the new characteristics of climate extremes can be quantified. Currently, emulators can rapidly generate simulations of future climate, but often to relatively low frequencies, as decadal, annual or monthly output at best in most cases, which is insufficient for studying extreme events that occur on a daily timescale. We show how it is possible to train a machine learning model to produce daily series of temperature or precipitation from monthly averages, thus facilitating a more robust investigation into how extreme events will change in the future. Key Points Earth system models (ESMs) are key devices for understanding how human actions will affect the future global climate Computational demands prevent us from running them for more than a handful of scenarios. ESM emulators are most commonly limited to the monthly frequency We present DiffESM as a data‐driven emulator of ESMs that closely matches the spatiotemporal distributions of ESMs at daily frequency

Injecting sulfate aerosol precursors into the stratosphere has been suggested as a means of geoengineering to cool the planet and reduce global warming. The decision to implement such a scheme would require a comparison of its benefits, dangers, and costs to those of other responses to global warming, including doing nothing. Here we evaluate those factors for stratospheric geoengineering with sulfate aerosols. Using existing U.S. military fighter and tanker planes, the annual costs of injecting aerosol precursors into the lower stratosphere would be several billion dollars. Using artillery or balloons to loft the gas would be much more expensive. We do not have enough information to evaluate more exotic techniques, such as pumping the gas up through a hose attached to a tower or balloon system. Anthropogenic stratospheric aerosol injection would cool the planet, stop the melting of sea ice and land‐based glaciers, slow sea level rise, and increase the terrestrial carbon sink, but produce regional drought, ozone depletion, less sunlight for solar power, and make skies less blue. Furthermore it would hamper Earth‐based optical astronomy, do nothing to stop ocean acidification, and present many ethical and moral issues. Further work is needed to quantify many of these factors to allow informed decision‐making.

Sulfate geoengineering (SG) methods based on lower stratospheric tropical injection of sulfur dioxide (SO2) have been widely discussed in recent years, focusing on the direct and indirect effects they would have on the climate system. Here a potential alternative method is discussed, where sulfur emissions are located at the surface or in the troposphere in the form of carbonyl sulfide (COS) gas. There are two time-dependent chemistry–climate model experiments designed from the years 2021 to 2055, assuming a 40 Tg-Syr-1 artificial global flux of COS, which is geographically distributed following the present-day anthropogenic COS surface emissions (SG-COS-SRF) or a 6 Tg-Syr-1 injection of COS in the tropical upper troposphere (SG-COS-TTL). The budget of COS and sulfur species is discussed, as are the effects of both SG-COS strategies on the stratospheric sulfate aerosol optical depth (∼Δτ=0.080 in the years 2046–2055), aerosol effective radius (0.46 µm), surface SOx deposition (+8.9 % for SG-COS-SRF; +3.3 % for SG-COS-TTL), and tropopause radiative forcing (RF; ∼-1.5 W m−2 in all-sky conditions in both SG-COS experiments). Indirect effects on ozone, methane and stratospheric water vapour are also considered, along with the COS direct contribution. According to our model results, the resulting net RF is −1.3 W m−2, for SG-COS-SRF, and −1.5 W m−2, for SG-COS-TTL, and it is comparable to the corresponding RF of −1.7 W m−2 obtained with a sustained injection of 4 Tg-Syr-1 in the tropical lower stratosphere in the form of SO2 (SG-SO2, which is able to produce a comparable increase of the sulfate aerosol optical depth). Significant changes in the stratospheric ozone response are found in both SG-COS experiments with respect to SG-SO2 (∼5 DU versus +1.4 DU globally). According to the model results, the resulting ultraviolet B (UVB) perturbation at the surface accounts for −4.3 % as a global and annual average (versus −2.4 % in the SG-SO2 case), with a springtime Antarctic decrease of −2.7 % (versus a +5.8 % increase in the SG-SO2 experiment). Overall, we find that an increase in COS emissions may be feasible and produce a more latitudinally uniform forcing without the need for the deployment of stratospheric aircraft. However, our assumption that the rate of COS uptake by soils and plants does not vary with increasing COS concentrations will need to be investigated in future work, and more studies are needed on the prolonged exposure effects to higher COS values in humans and ecosystems.

We test how the time of year of a large Arctic volcanic eruption determines the climate impacts by conducting simulations with a general circulation model of Earth's climate. For eruptions injecting less than about 3 Tg of SO2 into the lower stratosphere, we expect no detectable climatic effect, no matter what the season of the eruption. For an injection of 5 Tg of SO2 into the lower stratosphere, an eruption in the summer would cause detectable climate effects, whereas an eruption at other times of the year would cause negligible effects. This is mainly due to the seasonal variation in insolation patterns and sulfate aerosol deposition rates. In all cases, the sulfate aerosols that form get removed from the atmosphere within a year after the eruption by large‐scale deposition. Our simulations of a June eruption have many similar features to previous simulations of the eruption of Katmai in 1912, including some amount of cooling over Northern Hemisphere continents in the summer of the eruption, which is an expected climate response to large eruptions. Previous Katmai simulations show a stronger climate response, which we attribute to differences in choices of climate model configurations, including their specification of sea surface temperatures rather than the use of a dynamic ocean model as in the current simulations.

The paper constitutes Part 2 of a study performing a first systematic inter-model comparison of the atmospheric responses to stratospheric aerosol injection (SAI) at various single latitudes in the tropics, as simulated by three state-of-the-art Earth system models – CESM2-WACCM6, UKESM1.0, and GISS-E2.1-G. Building on Part 1 (Visioni et al., 2023) we demonstrate the role of biases in the climatological circulation and specific aspects of the model microphysics in driving the inter-model differences in the simulated sulfate distributions. We then characterize the simulated changes in stratospheric and free-tropospheric temperatures, ozone, water vapor, and large-scale circulation, elucidating the role of the above aspects in the surface SAI responses discussed in Part 1. We show that the differences in the aerosol spatial distribution can be explained by the significantly faster shallow branches of the Brewer–Dobson circulation in CESM2, a relatively isolated tropical pipe and older tropical age of air in UKESM, and smaller aerosol sizes and relatively stronger horizontal mixing (thus very young stratospheric age of air) in the two GISS versions used. We also find a large spread in the magnitudes of the tropical lower-stratospheric warming amongst the models, driven by microphysical, chemical, and dynamical differences. These lead to large differences in stratospheric water vapor responses, with significant increases in stratospheric water vapor under SAI in CESM2 and GISS that were largely not reproduced in UKESM. For ozone, good agreement was found in the tropical stratosphere amongst the models with more complex microphysics, with lower stratospheric ozone changes consistent with the SAI-induced modulation of the large-scale circulation and the resulting changes in transport. In contrast, we find a large inter-model spread in the Antarctic ozone responses that can largely be explained by the differences in the simulated latitudinal distributions of aerosols as well as the degree of implementation of heterogeneous halogen chemistry on sulfate in the models. The use of GISS runs with bulk microphysics demonstrates the importance of more detailed treatment of aerosol processes, with contrastingly different stratospheric SAI responses to the models using the two-moment aerosol treatment; however, some problems in halogen chemistry in GISS are also identified that require further attention. Overall, our results contribute to an increased understanding of the underlying physical mechanisms as well as identifying and narrowing the uncertainty in model projections of climate impacts from SAI.

The recent IPCC reports state that continued anthropogenic greenhouse gas emissions are changing the climate, threatening severe, pervasive and irreversible impacts. Slow progress in emissions reduction to mitigate climate change is resulting in increased attention to what is called geoengineering, climate engineering, or climate intervention – deliberate interventions to counter climate change that seek to either modify the Earth's radiation budget or remove greenhouse gases such as CO2 from the atmosphere. When focused on CO2, the latter of these categories is called carbon dioxide removal (CDR). Future emission scenarios that stay well below 2 °C, and all emission scenarios that do not exceed 1.5 °C warming by the year 2100, require some form of CDR. At present, there is little consensus on the climate impacts and atmospheric CO2 reduction efficacy of the different types of proposed CDR. To address this need, the Carbon Dioxide Removal Model Intercomparison Project (or CDRMIP) was initiated. This project brings together models of the Earth system in a common framework to explore the potential, impacts, and challenges of CDR. Here, we describe the first set of CDRMIP experiments, which are formally part of the 6th Coupled Model Intercomparison Project (CMIP6). These experiments are designed to address questions concerning CDR-induced climate reversibility, the response of the Earth system to direct atmospheric CO2 removal (direct air capture and storage), and the CDR potential and impacts of afforestation and reforestation, as well as ocean alkalinization.>

Atmospheric rivers (ARs), intrusions of warm and moist air, can effectively drive weather extremes over the Arctic and trigger subsequent impact on sea ice and climate. What controls the observed multi-decadal Arctic AR trends remains unclear. Here, using multiple sources of observations and model experiments, we find that, contrary to the uniform positive trend in climate simulations, the observed Arctic AR frequency increases by twice as much over the Atlantic sector compared to the Pacific sector in 1981-2021. This discrepancy can be reconciled by the observed positive-to-negative phase shift of Interdecadal Pacific Oscillation (IPO) and the negative-to-positive phase shift of Atlantic Multidecadal Oscillation (AMO), which increase and reduce Arctic ARs over the Atlantic and Pacific sectors, respectively. Removing the influence of the IPO and AMO can reduce the projection uncertainties in near-future Arctic AR trends by about 24%, which is important for constraining projection of Arctic warming and the timing of an ice-free Arctic. Arctic atmospheric rivers (ARs) have been increasing faster over the Atlantic sector than the Pacific sector in recent decades. The observed phase shift of interdecadal climate oscillations is key to explaining this disparity in Arctic AR trends.

Since the winter of 2013–2014, California has experienced its most severe drought in recorded history, causing statewide water stress, severe economic loss and an extraordinary increase in wildfires. Identifying the effects of global warming on regional water cycle extremes, such as the ongoing drought in California, remains a challenge. Here we analyse large-ensemble and multi-model simulations that project the future of water cycle extremes in California as well as to understand those associations that pertain to changing climate oscillations under global warming. Both intense drought and excessive flooding are projected to increase by at least 50% towards the end of the twenty-first century; this projected increase in water cycle extremes is associated with a strengthened relation to El Niño and the Southern Oscillation (ENSO)—in particular, extreme El Niño and La Niña events that modulate California’s climate not only through its warm and cold phases but also its precursor patterns. Identifying the effects of global warming on regional water cycle extremes, such as the ongoing drought in California, remains a challenge. Here, the authors present the results of multi-model simulations that project an increase in drought and flooding towards the end of the century.

Feedback control algorithms are important tools in climate intervention simulation design because they facilitate “top‐down” design, in which climate goals (often temperatures) are prescribed and a strategy chosen to meet the target. This approach is commonly used in simulations of stratospheric aerosol injection (SAI) interventions, but have never been used with marine cloud brightening (MCB) interventions. Using data from previously published MCB simulations, we use the Community Earth System Model (CESM2) to simulate MCB deployments over regions which expand with time to limit global warming to 1.5°C in the SSP2‐4.5 scenario, and we design a feedback control algorithm to determine the scope of intervention each year. Our methodology is able to control global mean temperature in this way, but controlling global mean temperature does not by itself mitigate regional impacts common to tropical MCB; additionally, the algorithm takes longer than intended to converge, indicating room for future improvement. Plain Language Summary When simulating solar climate intervention (the reflection of sunlight to cool the planet) with a climate model, it is sometimes desirable to design the experiment by first choosing a climate goal ‐ for example, limit global warming to 1.5°C above preindustrial ‐ and then choosing an intervention to meet that goal. However, figuring out what intervention is needed to meet that goal can be difficult and time‐consuming. When simulating an intervention via SAI, this process is commonly made easier with the use of a feedback algorithm: the intervention is adjusted “on the fly” during the simulation to drive the climate toward the desired state. This technique has never been used with a different type of intervention called MCB. In this study, we design a simulation where MCB is applied in a given region to cool the planet, and that region expands over time to keep global average temperature steady against global warming; we also design a feedback controller to regulate this process. Our results show that regulating global temperatures in this way is possible, but further research is needed to regulate regional temperatures in addition to just the global average. Key Points We design and simulate a climate intervention via marine cloud brightening which expands with time in order to counterbalance global warming We choose intervention rates by adapting a feedback control algorithm design previously used for stratospheric aerosol injection (SAI) simulations Our strategy manages global mean temperature (T0) but impacts are very different from SAI simulations that manage T0