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The explosive activity of the 2021 Tajogaite eruption eludes pigeonholing into well‐defined eruption styles, with a variety of pyroclast ejection modes occurring both alternately and simultaneously at multiple vents. Visually, we defined four endmembers of explosive activity, referred to as fountaining, spattering, ash‐poor jets and ash‐rich jets. To capture the physical parameters of these activities, we deployed a camera array including one high‐speed camera and three high‐definition cameras in two field campaigns. Transitions between and fluctuations within activity occurred at the time scale of minutes to hours, likely driven by the same shallow conduit and vent processes controlling Strombolian activity at other volcanoes, but at higher gas and magma fluxes. From a physical standpoint, mean pyroclast rise velocity ranged 5–50 m/s, maximum ejection velocity 10–220 m/s, and sub‐second mass flux of lapilli to bomb‐sized pyroclasts at the vent 0.2–200 × 103 kg/s. The largest mass flux occurred during fountaining, which contributed by far more than other activities to cone building. All explosive activity exhibited well‐defined pyroclast ejection pulses, and we found a positive correlation between the occurrence rate of ejection pulses and maximum pyroclast ejection velocity. Despite orders of magnitude variations, physical parameters shift gradually with no boundary from one activity endmember to another. As such, attributing this explosive activity specifically to any currently defined style variations is arbitrary and potentially misleading. The highly variable explosive activity of the Tajogaite eruption recalls previous definitions of violent Strombolian eruptions, an eruption style whose pyroclast ejection dynamics, however, were so far largely undefined. Plain Language Summary The 2021 Tajogaite volcanic eruption offered a rare opportunity to study in detail the physical properties and the controlling factors of explosive activity driven by basaltic magmas. The activity lasted almost uninterrupted for almost 3 months and had visually different manifestations occurring simultaneously and alternating at different volcanic vents. To study the explosive activity, we used one high‐speed camera, taking short, slow motion videos, and three commercial grade high‐definition camcorders recording for many hours. We found that the activity changed in features and intensity at the time scale of minutes to hours, largely controlled by changes in the size and debris cover of the vent, magma viscosity, and magma flux and gas content. The ejection velocity of large volcanic particles ranged 5–220 m/s, with mean values around 10–50 m/s. The mass flux of particles erupted reached peaks of 200 metric tons per second. Particle ejection was never steady but always proceeded in pulses, which were more frequent if the ejection velocity was higher. Our measurements show that the current classification schemes for explosive eruptions of basaltic magmas do not adequately describe the activity of the Tajogaite eruption, which represents a type of eruption that was not yet measured in detail. Key Points High‐definition and high‐speed imaging record the velocity, size, and mass flux of pyroclasts Activity shifted in location, nature and vigor at the time scale of hours and progressed in ejection pulses at the time scale of seconds Physical parameters of explosive activity vary gradually between apparently different activity styles, without any clear boundary

On 15 January 2022, Hunga Volcano in Tonga produced the most violent eruption in the modern satellite era, sending a water‐rich plume at least 58 km high. Using a combination of satellite‐ and ground‐based sensors, we investigate the astonishing rate of volcanic lightning (>2,600 flashes min−1) and what it reveals about the dynamics of the submarine eruption. In map view, lightning locations form radially expanding rings. We show that the initial lightning ring is co‐located with an internal gravity wave traveling >80 m s−1 in the stratospheric umbrella cloud. Buoyant oscillations of the plume's overshooting top generated the gravity waves, which enhanced turbulent particle interactions and triggered high‐current electrical discharges at unusually high altitudes. Our analysis attributes the intense lightning activity to an exceptional mass eruption rate (>5 × 109 kg s−1), rapidly expanding umbrella cloud, and entrainment of abundant seawater vaporized from magma‐water interaction at the submarine vent. Plain Language Summary The eruption of Tonga's underwater Hunga Volcano culminated on 15 January 2022 with a giant volcanic plume that rose out of the ocean and into the mesosphere. This plume created record‐breaking amounts of volcanic lightning observed both from space and by radio antennas on the ground thousands of kilometers away. We show that the eruption created more lightning than any storm yet documented on Earth, including supercells and tropical cyclones. The volcanic plume rose to its maximum height and expanded outward as an umbrella cloud, creating fast‐moving concentric ripples known as gravity waves, analogous to a rock dropped in a pond. Point locations of lightning flashes also expanded outward in a pattern of donut‐shaped rings, following the movement of these ripples. Optically bright lightning was detected at unusually high altitudes, in regions of the volcanic cloud 20–30 km above sea level. Our findings show that a sufficiently powerful volcanic plume can create its own weather system, sustaining the conditions for electrical activity at heights and rates not previously observed. Overall, remote detection of lightning contributed to a detailed timeline of this historic eruption and, more broadly, provides a valuable tool for monitoring and nowcasting hazards of explosive volcanism worldwide. Key Points This eruption produced the most intense lightning rates ever documented in Earth's atmosphere Lightning rings expand with enormous gravity waves in the umbrella cloud, caused by buoyant oscillation of the overshooting plume top Volcanic lightning and satellite analysis reveal at least four phases of eruptive activity from 02:57–15:12 UTC on 15 January 2022

We introduce a novel analytical expression that allows for fast assessment of mass flow rate of both vertically‐rising and bent‐over volcanic plumes as a function of their height, while first order physical insight is maintained. This relationship is compared with a one‐dimensional plume model to demonstrate its flexibility and then validated with observations of the 1980 Mount St. Helens and of the 2010 Eyjafjallajökull eruptions. The influence of wind on the dynamics of volcanic plumes is quantified by a new dimensionless parameter (Π) and it is shown how even vertically‐rising plumes, such as the one associated with the Mount St. Helens 1980 eruption, can be significantly affected by strong wind. Comparison between a one‐dimensional model and the analytical equation gives anR2‐value of 0.88, while existing expressions give negativeR2‐values due to their inability to adapt to different source and atmospheric conditions. Therefore, this new expression has important implications both for current strategies of real‐time forecasting of ash transport in the atmosphere and for the characterization of explosive eruptions based on the study of tephra deposits. In addition, this work provides a framework for the application of more complete three‐dimensional numerical models as it greatly reduces the parameter space that needs to be explored. Key Points New analytical expression to derive mass flow rate of volcanic plumes New dimensionless parameter to assess influence of wind on plume height Examination of mass flow rates associated with two important eruptions

Saltwater intrusion is a critical concern for coastal communities due to its impacts on fresh ecosystems and civil infrastructure. Declining recharge and rising sea level are the two dominant drivers of saltwater intrusion along the land‐ocean continuum, but there are currently no global estimates of future saltwater intrusion that synthesize these two spatially variable processes. Here, for the first time, we provide a novel assessment of global saltwater intrusion risk by integrating future recharge and sea level rise while considering the unique geology and topography of coastal regions. We show that nearly 77% of global coastal areas below 60° north will undergo saltwater intrusion by 2100, with different dominant drivers. Climate‐driven changes in subsurface water replenishment (recharge) is responsible for the high‐magnitude cases of saltwater intrusion, whereas sea level rise and coastline migration are responsible for the global pervasiveness of saltwater intrusion and have a greater effect on low‐lying areas. Plain Language Summary Coastal watersheds around the globe are facing perilous changes to their freshwater systems. Driven by climatic changes in recharge and sea level working in tandem, sea water encroaches into coastal groundwater aquifers and consequently salinizes fresh groundwater, in a process called saltwater intrusion. To assess the vulnerability of coastal watersheds to future saltwater intrusion, we applied projections of sea level and groundwater recharge to a global analytical modeling framework. Nearly 77% of the global coast is expected to undergo measurable salinization by the year 2100. Changes in recharge have a greater effect on the magnitude of salinization, whereas sea level rise drives the widespread extensiveness of salinization around the global coast. Our results highlight the variable pressures of climate change on coastal regions and have implications for prioritizing management solutions. Key Points First global analysis of future saltwater intrusion vulnerability responding to spatially variable recharge and sea level rise is provided Recharge drives the extreme cases of saltwater intrusion, while sea level rise is responsible for its global pervasiveness Nearly 77% of global coastal areas below 60° north will undergo saltwater intrusion by 2100

The eruption of Hunga volcano on 15 January 2022 produced a higher plume and faster-growing umbrella cloud than has ever been previously recorded. The plume height exceeded 58 km, and the umbrella grew to 450 km in diameter within 50 min. Assuming an umbrella thickness of 10 km, this growth rate implied an average volume injection rate into the umbrella of 330–500 km 3 s −1 . Conventional relationships between plume height, umbrella-growth rate, and mass eruption rate suggest that this period of activity should have injected a few to several cubic kilometers of rock particles (tephra) into the plume. Yet tephra fall deposits on neighboring islands are only a few centimeters thick and can be reproduced using ash transport simulations with only 0.1–0.2 km 3 erupted volume (dense-rock equivalent). How could such a powerful eruption contain so little tephra? Here, we propose that seawater mixing at the vent boosted the plume height and umbrella growth rate. Using the one-dimensional (1-D) steady plume model Plumeria, we find that a plume fed by ~90% water vapor at a temperature of 100 °C (referred to here as steam) could have exceeded 50 km height while keeping the injection rate of solids low enough to be consistent with Hunga’s modest tephra-fall deposit volume. Steam is envisaged to rise from intense phreatomagmatic jets or pyroclastic density currents entering the ocean. Overall, the height and expansion rate of Hunga’s giant plume is consistent with the total mass of fall deposits plus underwater density current deposits, even though most of the erupted mass decoupled from the high plume. This example represents a class of high (> 10 km), ash-poor, steam-driven plumes, that also includes Kīlauea (2020) and Fukutoku-oka-no-ba (2021). Their height is driven by heat flux following well-established relations; however, most of the heat is contained in steam rather than particles. As a result, the heights of these water-rich plumes do not follow well-known relations with the mass eruption rate of tephra.

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI‐based weather models produces medium‐range forecasts within seconds, with a skill similar to state‐of‐the‐art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI‐models toward process‐based evaluations lays the foundation for hazard‐driven applications. We assess the forecast skill of the top‐performing AI‐models GraphCast, Pangu‐Weather and FourCastNet for convective parameters at lead‐times up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu‐Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments. Plain Language Summary Over the past year, several global AI‐based weather models were released and produce a similar quality of forecasts as traditional weather models. AI‐models are very fast and computationally cheap to produce forecasts. The evaluation of AI‐models has largely focused on single atmospheric variables at certain heights. To forecast specific phenomena, such as thunderstorms, a combination of variables must be accurate at multiple heights. Here we use the output of AI‐models to derive thunderstorm‐related ingredients. We compare 10‐day‐forecasts between the AI‐models GraphCast, Pangu‐Weather and FourCastNet and a state‐of‐the‐art traditional weather model while using a reanalysis data set as the reference. The example of a tornado outbreak in the southern United States shows that all models are capable of forecasting thunderstorm ingredients multiple days in advance. To obtain a robust assessment, we evaluate the entire thunderstorm season in 2020 in North America, Europe, Argentina, and Australia, where severe thunderstorms occur frequently. Two of the three AI‐models achieve similar or even better results than the traditional weather model while being much cheaper to operate computationally. Forecasting thunderstorm parameters directly, instead of calculating them afterward, is likely to produce even better results. This opens opportunities for rapid and accessible forecasts for severe thunderstorm phenomena. Key Points AI‐based global weather models produce forecasts with sufficient accuracy to derive instability and shear metrics skillfully The best AI‐based weather models are capable of competing with state‐of‐the‐art numerical weather predictions of instability and shear This is a major step toward computationally inexpensive and fast convective outlooks

Volcanic activity has been shown to affect Earth's climate in a myriad of ways. One such example is that eruptions proximate to surface ice will promote ice melting. In turn, the crustal unloading associated with melting an ice sheet affects the internal dynamics of the underlying magma plumbing system. Geochronologic data from the Andes over the last two glacial cycles suggest that glaciation and volcanism may interact via a positive feedback loop. At present, accurate sea‐level predictions hinge on our ability to forecast the stability of the West Antarctic Ice Sheet, and thus require consideration of two‐way subglacial volcano‐deglaciation processes. The West Antarctic Ice Sheet is particularly vulnerable to collapse, yet its position atop an active volcanic rift is seldom considered. Ice unloading deepens the zone of melting and alters the crustal stress field, impacting conditions for dike initiation, propagation, and arrest. However, the consequences for internal magma chamber dynamics and long‐term eruption behavior remain elusive. Given that unloading‐triggered volcanism in West Antarctica may contribute to the uncertainty of ice loss projections, we adapt a previously published thermomechanical magma chamber model and simulate a shrinking ice load through a prescribed lithostatic pressure decrease. We investigate the impacts of varying unloading scenarios on magma volatile partitioning and eruptive trajectory. Considering the removal of km‐thick ice sheets, we demonstrate that the rate of unloading influences the cumulative mass erupted and consequently the heat released into the ice. These findings provide fundamental insights into the complex volcano‐ice interactions in West Antarctica and other subglacial volcanic settings. Plain Language Summary In regions like West Antarctica, volcanic eruptions occur underneath ice sheets. When hot magma comes in contact with ice, it can accelerate the melting of the ice cover. Beyond this, as climate change causes ice sheets to shrink, the decreasing weight on a volcano may affect its likelihood of erupting. The effects of ice loss above volcanoes on the underlying volcanic activity are not well understood. We conducted computer simulations to explore how gradual ice loss affects magma stored in the Earth's crust. We find that volcanoes beneath shrinking ice sheets are sensitive to the rate at which the ice sheet shrinks. As the ice melts away, the reduced weight on the volcano allows the magma to expand, applying pressure upon the surrounding rock that may facilitate eruptions. Additionally, the reduced weight from the melting ice above also allows dissolved water and carbon dioxide to form gas bubbles, which causes pressure to build up in the magma chamber and may eventually trigger an eruption. Under these conditions, we find that the removal of an ice sheet above a volcano results in more abundant and larger eruptions, which may potentially hasten the melting of overlying ice through complex feedback mechanisms. Key Points During deglaciation, the evolution of a crustal magma chamber beneath kilometers of ice is sensitive to the rate at which ice is removed A critical rate of unloading can trigger additional eruption events Ice unloading expedites the onset of volatile exsolution, with consequences for magma chamber pressurization and eruption size

Air‐sea heat and moisture fluxes modulate the surface energy balance and oceanic and atmospheric heat transport across all timescales. Spatial gradients of these fluxes, on a multitude of spatial scales, also have significant impacts on the ocean and atmosphere. Nevertheless, analysis of these gradients, and discussion regarding our ability to represent them, is relatively absent within the community. This letter discusses their importance and presents a wintertime climatology. Their sensitivity to spatiotemporal scale and choice of data set is also examined in the mid‐latitudes. A lead‐lag analysis illustrates that wintertime air‐sea heat flux gradients in the Gulf Stream can precede the North Atlantic Oscillation by ∼1 month. A lack of observations and thus validation of air‐sea heat flux gradients represents a significant gap in our understanding of how air‐sea processes affect weather and climate, and warrants increased attention from the observational and modeling communities. Plain Language Summary The oceans impact both weather and climate by heating and cooling the lower atmosphere. Surface latent (sensible) heat flux is a quantity that measures the exchange of heat associated with evaporation of seawater (an air‐sea temperature difference). In addition to the absolute exchange, the manner in which the exchange varies spatially (the heat flux gradients) is also known to be important for the development of weather systems and longer‐term climate. Despite this, relatively little attention is paid in the literature to variability in these gradients. This study provides a brief overview of their importance and provides a wintertime climatology in these gradients. It is also illustrated that when considering gradients, the importance of specifying the spatial scale over which the gradient is calculated is critical. Although many differences exist between air‐sea heat flux data products in these gradients, there are currently almost no observations to validate them in key areas of interest, which represents a significant deficiency in our understanding of ocean‐atmosphere interactions. This is emphasized by demonstrating that these gradients in the mid‐latitudes can statistically precede variability in the North Atlantic Oscillation, the most important mode of monthly atmospheric variability in the North Atlantic. Key Points Air‐sea heat and moisture flux gradients modulate important oceanic and atmospheric processes across a multitude of spatiotemporal scales Air‐sea heat flux gradient variability can statistically precede mid‐latitude atmospheric variability Notable air‐sea heat and moisture flux gradient inconsistencies exist in data products, yet the ability to validate them remains elusive

Pyroclastic material in the form of altered volcanic ash or tephra has been reported and described from one or more stratigraphic units from the Proterozoic to the Tertiary. This altered tephra, variously called bentonite or K-bentonite or tonstein depending on the degree of alteration and chemical composition, is often linked to large explosive volcanic eruptions that have occurred repeatedly in the past. K-bentonite and bentonite layers are the key components of a larger group of altered tephras that are useful for stratigraphic correlation and for interpreting the geodynamic evolution of our planet. Bentonites generally form by diagenetic or hydrothermal alteration under the influence of fluids with high-Mg content and that leach alkali elements. Smectite composition is partly controlled by parent rock chemistry. Studies have shown that K-bentonites often display variations in layer charge and mixed-layer clay ratios and that these correlate with physical properties and diagenetic history. The following is a review of known K-bentonite and related occurrences of altered tephra throughout the timescale from Precambrian to Cenozoic.

Anthropogenic aerosols (AER) and greenhouse gases (GHG)—the leading drivers of the forced historical change—produce different large‐scale climate response patterns, with correlations trending from negative to positive over the past century. To understand what caused the time‐evolving comparison between GHG and AER response patterns, we apply a low‐frequency component analysis to historical surface ocean changes from CESM1 single‐forcing large‐ensemble simulations. While GHG response is characterized by its first leading mode, AER response consists of two distinct modes. The first one, featuring long‐term global AER increase and global cooling, opposes GHG response patterns up to the mid‐twentieth century. The second one, featuring multidecadal variations in AER distributions and interhemispheric asymmetric surface ocean changes, appears to reinforce the GHG warming effect over recent decades. AER thus can have both competing and synergistic effects with GHG as their emissions change temporally and spatially. Plain Language Summary Anthropogenically forced climate change over the past century has been mainly caused by two types of emissions: greenhouse gases (GHG) and aerosols (AER). In general, sulfate aerosols from industrial sources can reflect shortwave radiation to yield a cooling effect opposite to the GHG warming effect. However, model simulations isolating GHG and AER forcings show that the large‐scale climate effect of AER does not always dampen the GHG effect. Instead, over recent decades, AER have produced surface ocean response patterns more like the GHG response. Using a novel low‐frequency statistical decomposion, we find that aerosols have driven two distinct modes of climate change patterns over the historical period. The first mode is associated with global aerosol increase, resulting in global‐wide cooling damping the GHG‐induced warming. The second mode is associated with the shift in aerosol emissions from north America/western Europe to southeast Asia, which drives regional changes enhancing the GHG effect. Our results highlight the importance of considering the temporal and spatial evolutions of AER emissions in assessing GHG and AER climate effects and attributing historical anthropogenic climate changes to GHG and AER forcings. Key Points Over the past century, GHG forced response is characterized by a single dominant mode while AER response consists of two distinct modes Monotonic global aerosol increases, mainly from Southeast Asia emissions, produce a global aerosol cooling mode opposing greenhouse warming Important in recent decades, geographic redistribution of AER emissions produces a second aerosol mode that reinforces greenhouse warming