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Changes in precipitation patterns with climate change could have important impacts on human and natural systems. Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) report trends in daily precipitation patterns over the last five decades in the western United States, focusing on meteorological drought. They report that dry intervals (calculated at the annual or seasonal level) have increased across much of the southwestern U.S., with statistical assessment suggesting the results are statistically robust. However, Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) preprocess their annual (or seasonal) averages to compute 5‐year moving window averages before using established statistical techniques for trend analysis that assume independence about some fixed trend. Here we show that the moving window preprocessing violates that independence assumption and inflates the statistical significance of their trend estimates. This raises questions about the robustness of their results. We conclude by discussing the difficulty of adjusting for spatial structure when assessing time trends in a regional context. Plain Language Summary: A recent paper reports trends in drought in the western United States, in particular increases in drought in the southwestern United States, based on changes in the lengths of time intervals without precipitation. In this "comment" we note that the preprocessing approach used in the paper artificially increases the apparent statistical signal in the data and caution that the evidence for the trends reported is not as strong as presented in the paper. We conclude by discussing the difficulty of estimating trends in a statistically rigorous fashion across multiple weather stations. Key Points: Statistical uncertainty about trends in droughts in the southwestern US is larger than reported in a recent GRL letter [ABSTRACT FROM AUTHOR]

The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi‐model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA‐2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design. Plain Language Summary: Atmospheric rivers (ARs) are a type of weather pattern known to be important for moving water from the warm, moist tropics to the cool, dry polar regions; when they reach midlatitudes in the winter time, they are commonly associated with heavy precipitation. Recent studies that assess the impacts of global climate change on ARs tend to agree that there will be more ARs in a warmer climate, and that ARs will tend to be more extreme. However, it has been increasingly recognized by the AR research community that these results may depend on the method used to identify ARs and the choice of climate model. This study reports results from a controlled experiment, involving an international research community, that aims to show how different AR identification methods and climate models might impact our scientific understanding of ARs in the future. Results show that there will likely be more ARs in the future, and that ARs will generally have a larger spatial footprint. This experiment also shows that uncertainty in these results are large, with the uncertainty from AR identification methods outweighing that of climate models. Future efforts to better understand the physics of ARs may help us reduce this uncertainty. Key Points: Uncertainty associated with atmospheric river (AR) definition dominates model uncertainty for projections of Pacific and Atlantic landfalling ARsMost AR detection algorithms show an increase in AR frequency in future simulationsAR statistics in CMIP 5‐and‐6 models compare remarkably well with reanalysis [ABSTRACT FROM AUTHOR]

Multidecadal and longer changes to the Atlantic interhemispheric sea surface temperature gradient (AITG) in phase 5 of the Coupled Model Intercomparison Project (CMIP5) historical simulations are investigated. Observations show a secular trend to this gradient over most of the twentieth century, with the southern lobe warming faster relative to its northern counterpart. A previous study of phase 3 of the CMIP (CMIP3) suggests that this trend is partially forced by anthropogenic sulfate aerosols. This analysis collectively confirms the partially forced trend for the CMIP5 and by anthropogenic aerosols. Like the CMIP3, the CMIP5 also simulates a reversal in the AITG trend in the late 1970s, which was attributed to a leveling off of the anthropogenic aerosol influence and increased influence of greenhouse gases in the late twentieth century. Two (of 25) CMIP5 models, however, systematically simulate a twentieth-century trend opposite to observed, leading to some uncertainty regarding the forced nature of the AITG trend. The observed AITG also exhibits a pronounced multidecadal modulation on top of the trend, associated with the Atlantic multidecadal oscillation (AMO). Motivated by a recent suggestion that the AMO is a forced response to aerosols, the causes of this multidecadal behavior were also examined. A few of the CMIP5 models analyzed do produce multidecadal AITG variations that are correlated to the observed AMO-like variation, but only one, the Hadley Centre Global Environmental Model, version 2 (HadGEM2), systematically simulates AMO-like behavior with both the requisite amplitude and phase. The CMIP5 simulations thus point to a robust aerosol influence on the historical AITG trend but not to the AMO-like multidecadal behavior. [ABSTRACT FROM AUTHOR]

The tropical Atlantic interhemispheric gradient in sea surface temperature significantly influences the rainfall climate of the tropical Atlantic sector, including droughts over West Africa and Northeast Brazil. This gradient exhibits a secular trend from the beginning of the twentieth century until the 1980s, with stronger warming in the south relative to the north. This trend behavior is on top of a multidecadal variation associated with the Atlantic multidecadal oscillation. A similar long-term forced trend is found in a multimodel ensemble of forced twentieth-century climate simulations. Through examining the distribution of the trend slopes in the multimodel twentieth-century and preindustrial models, the authors conclude that the observed trend in the gradient is unlikely to arise purely from natural variations; this study suggests that at least half the observed trend is a forced response to twentieth-century climate forcings. Further analysis using twentieth-century single-forcing runs indicates that sulfate aerosol forcing is the predominant cause of the multimodel trend. The authors conclude that anthropogenic sulfate aerosol emissions, originating predominantly from the Northern Hemisphere, may have significantly altered the tropical Atlantic rainfall climate over the twentieth century. [ABSTRACT FROM AUTHOR]

In a recent multimodel detection and attribution (D&A) study using the pooled results from 22 different climate models, the simulated "fingerprint" pattern of anthropogenically caused changes in water vapor was identifiable with high statistical confidence in satellite data. Each model received equal weight in the D&A analysis, despite large differences in the skill with which they simulate key aspects of observed climate. Here, we examine whether water vapor D&A results are sensitive to model quality. The "top 10" and "bottom 10" models are selected with three different sets of skill measures and two different ranking approaches. The entire D&A analysis is then repeated with each of these different sets of more or less skillful models. Our performance metrics include the ability to simulate the mean state, the annual cycle, and the variability associated with El Wino. We find that estimates of an anthropogenic water vapor fingerprint are insensitive to current model uncertainties, and are governed by basic physical processes that are well-represented in climate models. Because the fingerprint is both robust to current model uncertainties and dissimilar to the dominant noise patterns, our ability to identify an anthropogenic influence on observed multidecadal changes in water vapor is not affected by "screening" based on model quality. [ABSTRACT FROM AUTHOR]

Data from the satellite-based Special Sensor Microwave Imager (SSM/l) show that the total atmospheric moisture content over oceans has increased by 0.41 kg/m2 per decade since 1988. Results from current climate models indicate that water vapor increases of this magnitude cannot be explained by climate noise alone. In a formal detection and attribution analysis using the pooled results from 22 different climate models, the simulated ‘fingerprint’ pattern of anthropogenically caused changes in water vapor is identifiable with high statistical confidence in the SSM/l data. Experiments in which forcing factors are varied individually suggest that this fingerprint ‘match’ is primarily due to human-caused increases in greenhouse gases and not to solar forcing or recovery from the eruption of Mount Pinatubo. Our findings provide preliminary evidence of an emerging anthropogenic signal in the moisture content of earth's atmosphere. [ABSTRACT FROM AUTHOR]

Previous research has identified links between changes in sea surface temperature (SST) and hurricane intensity. We use climate models to study the possible causes of SST changes in Atlantic and Pacific tropical cyclogenesis regions. The observed SST increases in these regions range from 0.32°C to 0.67°C over the 20th century. The 22 climate models examined here suggest that century-time-scale SST changes of this magnitude cannot be explained solely by unforced variability of the climate system. We employ model simulations of natural internal variability to make probabilistic estimates of the contribution of external forcing to observed SST changes. For the period 1906–2005, we find an 84% chance that external forcing explains at least 67% of observed SST increases in the two tropical cyclogenesis regions. Model ‘20th-century’ simulations, with external forcing by combined anthropogenic and natural factors, are generally capable of replicating observed SST increases. In experiments in which forcing factors are varied individually rather than jointly, human-caused changes in greenhouse gases are the main driver of the 20th-century SST increases in both tropical cyclogenesis regions. [ABSTRACT FROM AUTHOR]

We examine simulations of today's climate performed with a global atmospheric general circulation model run at spectral truncations of T42, T170, and T239, corresponding to grid cell sizes of roughly 310 km, 75 km, and 55 km, respectively. The simulations were forced with observed sea-surface temperatures and sea-ice concentrations. The T42 simulations and initial simulations at T170 and T239 were performed using a model version that was carefully "tuned" to optimize results at T42; subsequent simulations at T170 and T239 used a model version that was partly re-tuned to improve results at T170. On the scales of a T42 grid cell and larger, nearly all quantities we examined in all the T170 and T239 simulations agree better with observations, at least in terms of spatial patterns, than in the T42 simulations. In some cases the improvements are very substantial. Improvements are seen in all-season, global domain results, and in results pertaining to most seasons and latitude bands. Increasing the model resolution from T42 introduces biases (errors in the mean) into some simulated quantities; the worst of these were removed by the partial retuning we performed at T170. This retuning has little effect on the spatial patterns of results, except in Northern Hemisphere winter at T170, where it tends to bring improvements. We discuss aspects of simulated regional climates, and their dependence on model resolution. [ABSTRACT FROM AUTHOR]

The importance of extreme event attribution rises as climate change causes severe damage to populations resulting from unprecedented events. In February 2019, a planetary wave shifted along the U.S.–Canadian border, simultaneously leading to troughing with anomalous cold events and ridging over Alaska and northern Canada with abnormal warm events. Also, a dry-stabilized anticyclonic circulation over low latitudes induced warm extreme events over Mexico and Florida. Most attribution studies compare the climate model simulations under natural or actual forcing conditions and assess probabilistically from a climatological point of view. However, in this study, we use multiple ensembles from an operational forecast model, promising statistical as well as dynamically constrained attribution assessment, often referred to as the storyline approach to extreme event attribution. In the globally averaged results, increasing CO2 concentrations lead to distinct warming signals at the surface, resulting mainly from diabatic heating. Our study finds that CO2-induced warming eventually affects the possibility of extreme events in North America, quantifying the impact of anthropogenic forcing over less than a week's forecast simulation. Our study assesses the validity of the storyline approach conditional on the forecast lead times, which is hindered by rising noise in CO2 signals and the declining performance of the forecast model. The forecast-based storyline approach is valid for at least half of the land area within a 6-day lead time before the target extreme occurrence. Our attribution results highlight the importance of achieving net-zero emissions ahead of schedule to reduce the occurrence of severe heatwaves. [ABSTRACT FROM AUTHOR]

We evaluate tropical cyclones (TCs) in a set of thermodynamic global warming (TGW) simulations over the continental United States (CONUS). A 12 km simulation forced by ERA5 provides a 40‐year historical (1980–2019) control. Four complimentary future scenarios are generated using thermodynamic deltas applied to lateral boundary, interior, and surface forcing. We curate a data set of 4,498 6‐hourly TC snapshots in the control and find a corresponding "twin" in each counterfactual, permitting a paired comparison. Warming results in an increase in mean dynamical TC intensity and moisture‐related quantities, with the latter being more pronounced. TC inner cores contract slightly but outer storm size remains unchanged. The frequency with which TCs become more intense is only moderately consistent, with snapshots having increased hazards ranging from 50% to 80% depending on warming level. The fractions of TCs undergoing rapid intensification and weakening both increase across all warming simulations, suggesting elevated short‐term intensity variability. Plain Language Summary: We examined how tropical cyclones (TCs) near the United States might change due to warmer climates. First, we recreated past weather conditions from 1980 to 2019 using a specialized climate model. Then, we predicted future changes in the same meteorology by modifying the model to include warmer temperatures and running four additional simulations. We tracked and analyzed over 4,000 instances of TCs from the past 40 years, ensuring that these TCs were all matched between all five simulations, and evaluated their changes under future warming conditions. Our findings suggest that TCs will become stronger on average as the world warms, with the most notable increases in TC moisture. Not every TC in our study becomes more intense with warming, however, highlighting the complexity of understanding how extreme weather will change in the future. We found that the chances of a TC rapidly intensifying or suddenly weakening both rise with higher levels of warming. These trends could complicate predictions of TC intensity in the future, presenting additional challenges for forecasting and preparation. Key Points: Thermodynamic global warming simulations provide a unique large‐sample size view into paired counterfactual tropical cyclones12 km simulations indicate future near‐United States cyclones will, on average, be wetter, slightly more intense, but similarly sizedPeriods of rapid intensification and weakening indicate increased tropical cyclone intensity variability in warmer climates [ABSTRACT FROM AUTHOR]

The legal systems for ocean governance and climate change governance are based on the United Nations Convention on the Law of the Sea and the United Nations Framework Convention on Climate Change, respectively. However, due to differences in their negotiation backgrounds, legal scope, goals, and tasks, there is a lack of interaction between the two at the legal system level. The ocean plays a crucial role in regulating the Earth's climate system, yet its value is often underestimated in the United Nations Framework Convention on Climate Change. The aim of this study is to analyze the effectiveness of the United Nations Convention on the Law of the Sea in addressing climate change. Specifically, we will examine the Convention's ability to mitigate and adapt to climate change, and identify areas where it falls short, such as inadequate regulation of sea level rise, ocean acidification, and ocean fertilization. Based on this, proposals for governance paths from the perspective of the United Nations Convention on the Law of the Sea include developing the Agreement relating to the climate change and ocean governance and reinterpreting the United Nations Convention on the Law of the Sea in accordance with the Paris Agreement. The content should be adapted more flexibly to current climate change challenges, and provisions related to sea level rise and maritime boundaries should be reinterpreted to fill legal gaps. In addition, it is important to establish coordinated regulatory rules and framework agreements to address the issues of ocean fertilization and ocean acidification. Finally, to remedy the shortcomings in proving causation, scientific theories and due diligence obligations should be attributed. Through these measures, effective ocean law governance paths that address climate change can be explored. [ABSTRACT FROM AUTHOR]

High-amplitude quasi-stationary atmospheric Rossby waves with zonal wave numbers 6–8 associated with the phenomenon of quasi-resonant amplification (QRA) have been linked to persistent summer extreme weather events in the Northern Hemisphere. QRA is not well-resolved in current generation climate models, therefore, necessitating an alternative approach to assessing their behavior. Using a previously-developed fingerprint-based semi-empirical approach, we project future occurrence of QRA events based on a QRA index derived from the zonally averaged surface temperature field, comparing results from CMIP 5 and 6 (Coupled Model Intercomparison Project). There is a general agreement among models, with most simulations projecting substantial increase in QRA index. Larger increases are found among CMIP6-SSP5-8.5 (42 models, 46 realizations), with 85% of models displaying a positive trend, as compared with 60% of CMIP5-RCP8.5 (33 models, 75 realizations), with a reduced spread among CMIP6-SSP5-8.5 models. CMIP6-SSP3-7.0 (23 models, 26 realizations) simulations display qualitatively similar behavior to CMIP6-SSP5-8.5, indicating a substantial increase in QRA events under business-as-usual emissions scenarios, and the results hold regardless of the increase in climate sensitivity in CMIP6. Projected aerosol reductions in CMIP6-SSP3-7.0-lowNTCF (5 models, 16 realizations) lead to halting effect in QRA index and Arctic Amplification during the 1st half of the twenty-first century. Our analysis suggests that anthropogenic warming will likely lead to an even more substantial increase in QRA events (and associated summer weather extremes) than indicated by past analyses. [ABSTRACT FROM AUTHOR]

Heat is associated with increased risk of morbidity and mortality. People who are incarcerated are especially vulnerable to heat exposure due to demographic characteristics and their conditions of confinement. Evaluating heat exposure in prisons, and the characteristics of exposed populations and prisons, can elucidate prison‐level risk to heat exposure. We leveraged a high‐resolution air temperature data set to evaluate short and long‐term patterns of heat metrics for 1,614 prisons in the United States from 1990 to 2023. We found that the most heat‐exposed facilities and states were mostly in the Southwestern United States, while the prisons with the highest temperature anomalies from the historical record were in the Pacific Northwest, the Northeast, Texas, and parts of the Midwest. Prisons in the Pacific Northwest, the Northeast, and upper Midwest had the highest occurrences of days associated with an increased risk of heat‐related mortality. We also estimated differences in heat exposure at prisons by facility and individual‐level characteristics. We found higher proportions of non‐white and Hispanic populations in the prisons with higher heat exposure. Lastly, we found that heat exposure was higher in prisons with any of nine facility‐level characteristics that may modify risk to heat. This study brings together distinct measures of exposure, vulnerability, and risk, which would each inform unique strategies for heat‐interventions. Community leaders and policymakers should carefully consider which measures they want to apply, and include the voices of directly impacted people, as the differing metrics and perspectives will have implications for who is included in fights for environmental justice. Plain Language Summary: Heat is a direct and increasing threat to human health. People in prison are especially vulnerable to heat as an increasingly older and disabled population with limited agency over their conditions of confinement, healthcare, or access to resources to decrease heat exposure. We use an air temperature data set to measure short and long‐term patterns of various heat metrics for 1,614 prisons in the United States from 1990 to 2023. We find that the patterns of highs and lows greatly differ based on the metric of choice. We also estimated differences in heat exposure at prisons by facility and individual‐level characteristics. We found higher proportions of non‐white and Hispanic populations in prisons with higher heat exposure. We also found higher temperatures are in prisons that have characteristics that can modify exposure or vulnerability to increase overall risk. Distinct measures of exposure, vulnerability, and risk can each inform unique strategies for heat‐interventions in United States prisons. Community leaders and policymakers should carefully consider which measures they want to apply, and include the voices of directly impacted people, as the differing metrics and perspectives will have implications for which populations and prisons are included in efforts to reduce heat risk. Key Points: Prisons, incarcerated populations, and staff are exposed to heat and changing climates as measured through a variety of metricsHigher temperatures are found in prison landscapes that have characteristics that can modify exposure or vulnerability, increasing overall riskDistinct measures of exposure, vulnerability, and risk, can each inform unique strategies for heat‐interventions in US prisons [ABSTRACT FROM AUTHOR]

Observations and climate projections suggest a larger increase in tropical cyclone (TC)‐induced rainfall than that can be explained by the Clausius‐Clapeyron relationship of 7% increase in vapor content for each 1°C degree rise in temperature. However, these studies using diverse data sources and methods over various periods show inconsistencies regarding the location of this increase ‐ whether in the TC inner core or outer regions ‐ and offer differing explanations for the reported trends. This study uses the Pseudo‐global warming methodology on simulations of 117 western North Pacific TCs making landfall in Southeast Asia to investigate changes in TC rainfall structure by the end of the century under the SSP2‐4.5 and SSP3‐7.0 scenarios. Specifically, it tests the sensitivity of changing trends to various analysis methods used in previous studies and identifies the underlying physical mechanisms driving these changes. The findings indicate an amplified increase in rainfall in the TC inner core across all future scenarios, along with potentially decreased rainfall in the outer region under certain future climate conditions. Among TC categories, Supertyphoons exhibit the most significant increased rainfall across future states. Changes in TC primary and secondary circulations, TC structure, and the convergence of heat and moisture are the main factors shaping future rainfall patterns, outweighing the effects of changes in atmospheric and convective stability. Plain Language Summary: Tropical cyclone (TC)‐related rainfall is increasing with global warming. Typically, a 1°C increase in temperature leads to about a 7% increase in the atmosphere's water vapor holding capacity. However, the rise in TC rainfall outpaces this rate. Observations indicate this enhanced increase occurs in the TC outer region due to greater environmental moisture. Contrarily, modeling future projections suggest this concentrates in the inner core due to intensified TCs. This study analyzes a large data set of 819 simulations from 117 TCs making landfall in Southeast Asia to capture changes in future rainfall patterns under two contemporary climate change scenarios. Our findings reveal that the enhanced increase occurs in the TC inner core in all future states, while rainfall in the outer region decreases under specific climate conditions. Supertyphoons generate the heaviest rainfall, with minimal variations observed across climate scenarios. Variations of all dynamic and thermodynamic factors closely tied to TC rainfall are investigated to provide a comprehensive picture of the physics behind the changes. The interplay between TC dynamics (e.g., primary and secondary circulations), TC structure, and thermodynamic conditions (e.g., convergence of moisture, and temperatures), plays a critical role in the changing behaviors of TC rainfall. Key Points: Rainfall increases in the cyclone's inner core across all future states but decreases in the outer region under specific statesVariations in cyclone circulations, structure, convergence of moisture and heat in future climates drive rainfall changing patternsSupertyphoons cause the heaviest rainfall compared to other categories, with slight variations noted across climate conditions [ABSTRACT FROM AUTHOR]

While extreme weather events are often localized, the potential effects on global forests can be far reaching due to the interconnected nature of forest product markets. To better understand these dynamics, this study leverages historical forest‐based wind damage data in the United States and applies this information as shocks within a global forest sector outlook model. A large, localized wind event modeled as a shock to the US South creates a one‐time increase of 18.7 million m3 from salvage harvest operations, equal to over 4% of national harvest. This crowds out traditional harvest activities, leading to downward pressure on prices in the short run, followed by a persistent effect that could take approximately 25 years to dissipate from markets. Average annual wind damage contributes downward pressure on roundwood prices between 1% and 4% in the United States, and this effect is distributed to other countries. The findings suggest that large, localized shocks reverberate across regions and wood product markets due to their interconnected supply chains and trade patterns, and these impacts have important temporal dynamics. Another key result is that the magnitude of these effects are offset by endogenous market reactions in other markets. In other words, unaffected regions change their harvesting patterns in order to compensate for changes in the availability of fiber, shedding light on the importance of capturing global channels as large shocks materialize in changes in market dynamics internationally. Monte Carlo simulations suggest a wide confidence band on salvage harvest rates and prices. Plain Language Summary: Extreme weather events can have significant impacts on global forests despite being localized, thanks to the interconnected nature of forest product markets. This study investigates this relationship by analyzing historical wind damage data from US forests. It finds that a major wind event in the US South led to a substantial increase in salvage harvest, equivalent to over 4% of the national harvest. This surge in harvest disrupted traditional activities, causing short‐term price drops and long‐lasting effects over about 25 years due to tree loss. Wind damage annually contributes to 1%–4% price decreases in US roundwood, affecting other countries too. The study emphasizes how such shocks ripple across regions and markets due to intertwined supply chains and trade patterns. It also highlights how unaffected regions adapt their harvesting to balance fiber availability, underscoring the importance of understanding global dynamics. Monte Carlo simulations reveal wide confidence bands on salvage harvest rates and prices, underlining the uncertainty surrounding these estimates. Key Points: Extreme weather events, while localized, but can have far reaching consequences on forests due to the interconnected wood product marketsThe effect of extreme weather events on wood product markets is mitigated through endogenous market reactions in other regions [ABSTRACT FROM AUTHOR]

Cyclone Gabrielle passed along the northern coast of Aotearoa New Zealand in February 2023, producing historic rainfall accumulations and impacts. Gabrielle was an ex‐tropical cyclone that stalled and re‐energised off the north coast, resembling descriptions of worst case scenarios for the northeast of the country. Here we report on a comparison of the actual forecast of the storm against forecasts under conditions representative of a climate without anthropogenic interference and of a climate +2.0°C warmer than pre‐industrial (about 1.0°C cooler and warmer than present respectively). We find that regional total rainfall accumulations from a Gabrielle‐like storm are about 10% higher because of the historical anthropogenic warming, and will increase by a larger amount under similar future warming. These differences are driven by a 20% (relative to a non‐anthropogenic world) to 30% (relative to a +2.0°C world) rise in peak rainfall rates, which in turn is mainly driven by a more temporally concentrated column‐integrated moisture flux. The forecast model generates the larger increase for the +2.0°C world through greater precipitation efficiency, reflecting the importance of unresolved precipitation processes in the climate change response of rainfall extremes. Plain Language Summary: Tropical Cyclone Gabrielle formed in the Coral Sea in February 2023, then moved southeast and passed along the northern coast of Aotearoa New Zealand as an ex‐tropical cyclone. The storm's rainfall produced one of the worst natural disasters in the country's history. We compare the weather forecast of the storm against forecasts in which past anthropogenic warming is removed and in which future warming is added. We find that the storm would have dumped about 10% less total rainfall and 20% less peak hourly rainfall without human interference. A similar future amount of warming will result in a comparable total increase in storm rainfall but with about a 30% increase in the peak hourly rate. Key Points: Cyclone Gabrielle delivered large amounts of rain to northeastern Aotearoa New Zealand in February 2023Anthropogenic warming increases the total amount of rain delivered by a Gabrielle‐like storm by about 10%/°CAnthropogenic warming increases the peak amount of rain delivered by a Gabrielle‐like storm by about 20%–30%/°C [ABSTRACT FROM AUTHOR]

Current global historical reanalyzes prevent to adequately examine the role of the near‐core surface wind structural properties on tropical cyclones climate trends. Here we provide theoretical and observational evidences that they are crucial for the monitoring of integrated kinetic energy. The kinetic energy balance is reduced to a simple rule involving two parameters characterizing the surface wind structure and directly suggested by the governing equations. The theory is uniquely verified with a database of high‐resolution ocean surface winds estimated from all‐weather spaceborne synthetic aperture radar. Such measurements provide indirect estimates of a multiplicative constant modulating the kinetic energy balance and associated with the system thermodynamics. Consequently, accumulated high‐resolution acquisitions of the ocean surface shall allow to better monitor the integrated kinetic energy and provide new means to tackle climatological studies of tropical cyclones destructiveness. Plain Language Summary: Studying the long‐term climate trends of tropical cyclones is challenging because the historical data is not always reliable. One particular issue concerns the accurate reporting of surface wind properties near the core of these storms in past and present records. This study uses both theory and high‐resolution surface wind observations from satellite radar to highlight the importance of investigating these properties, specifically for monitoring the total energy, which is a measure of a storm destructive potential. Two spatial scales describing the tropical cyclone wind structure are identified and may be efficiently measured thanks to the high‐resolution sensor. The storm energy equilibrium is shown to be controlled by these two spatial scales, in both theory and observations. This equilibrium is also influenced by the temperature characteristics of a storm, which are themselves modulated by environmental and climatological conditions. Consequently, future high‐resolution observations from the satellite radar should help better understanding the dependence of integrated kinetic energy with space and time. Key Points: High‐resolution spaceborne synthetic aperture radar measurements inform on the tropical cyclone kinetic energy balanceThe tropical cyclone integrated kinetic energy balance is controlled by the surface wind decay and thermodynamical characteristicsAccumulating high‐resolution surface wind measurements shall allow to better assess trends in the tropical cyclone destructive potential [ABSTRACT FROM AUTHOR]

Introduction: Coastal marine ecosystems, are particularly susceptible to climate change. One such threat is atmospheric heatwaves, which are predicted to increase in frequency, duration, and intensity. Many intertidal organisms already live at the edge of their thermal tolerance limits and heatwaves can outstretch an organism's ability to compensate in the short term. In June 2021 the Pacific Northwest region of North America, including the Salish Sea, experienced a significant atmospheric heatwave during some of the lowest tides of the year. This was followed by numerous reports of dead and dying intertidal marine organisms region-wide. A semi-quantitative rapid assessment found a range of both species- and location-specific effects but generally recorded widespread negative impacts to intertidal shellfish species across the Salish Sea. Methods: Following these results, we opportunistically analyzed data collected by intertidal bivalve resource managers from the region. These datasets allowed us to examine regional density and size data for clam and oyster populations before and after the heatwave to increase our quantitative understanding of heatwave effects. Results: We found a range of responses including positive and negative effects of the heatwave on clam and oyster density. While we generally found small changes in bivalve size, some site-species combinations displayed large shifts in size frequency. Many of our analyses did not indicate even moderate statistical support, even with large changes in the mean, driven in part by high variability in the data. Time intervals between surveys, ranging from 2 to over 25 months, had little effect on observed variability indicating that any heatwave-induced effects may be masked by variability inherent to the population ecology and/or survey methodology. Discussion: This analysis has highlighted the need for intertidal resource managers, and the greater research community, to consider alternative survey approaches designed to constrain variability in order to detect the effects of acute or extreme events. With the effects of climate change predicted to become more intense, targeted survey approaches may be needed to detect the effects and implications of such events and to continue effective management of intertidal bivalves in the Salish Sea and worldwide. [ABSTRACT FROM AUTHOR]

Since 2011, the Global Energy and Water cycle Exchanges (GEWEX) Water Vapor Assessment (G-VAP) has provided performance analyses for state-of-the-art reanalysis and satellite water vapour products to the GEWEX Data and Analysis Panel (GDAP) and the user community in general. A significant component of the work undertaken by G-VAP is to characterise the quality and uncertainty of these water vapour records to (i) ensure full exploitation and (ii) avoid incorrect use or interpretation of results. This study presents results from the second phase of G-VAP, where we have extended and expanded our analysis of total column water vapour (TCWV) from phase 1, in conjunction with updating the G-VAP archive. For version 2 of the archive, we consider 28 freely available and mature satellite and reanalysis data products, remapped to a regular longitude–latitude grid of 2° × 2° and on monthly time steps between January 1979 and December 2019. We first analysed all records for a "common" short period of 5 years (2005–2009), focusing on variability (spatial and seasonal) and deviation from the ensemble mean. We observed that clear-sky daytime-only satellite products were generally drier than the ensemble mean, and seasonal variability/disparity in several regions up to 12 kg m -2 related to original spatial resolution and temporal sampling. For 11 of the 28 data records, further analysis was undertaken between 1988–2014. Within this "long period", key results show (i) trends between - 1.18 ± 0.68 to 3.82 ± 3.94 kg m -2 per decade and - 0.39 ± 0.27 to 1.24 ± 0.85 kg m -2 per decade were found over ice-free global oceans and land surfaces, respectively, and (ii) regression coefficients of TCWV against surface temperatures of 6.17 ± 0.24 to 27.02 ± 0.51 % K -1 over oceans (using sea surface temperature) and 3.00 ± 0.17 to 7.77 ± 0.16 % K -1 over land (using surface air temperature). It is important to note that trends estimated within G-VAP are used to identify issues in the data records rather than analyse climate change. Additionally, breakpoints have been identified and characterised for both land and ocean surfaces within this period. Finally, we present a spatial analysis of correlations to six climate indices within the long period, highlighting regional areas of significant positive and negative correlation and the level of agreement among records. [ABSTRACT FROM AUTHOR]

Flood variability associated with urbanization, ecological change, and climatic change is of increasing economic and social concern in and around Flagstaff, Arizona, where flood hydrology is influenced by a biannual precipitation regime and the relatively unique geologic setting at the edge of the San Francisco Volcanic Field on the southern edge of the Colorado Plateau. There has been limited long-term gauging of the ephemeral channels draining the developed lands and dry coniferous forests of the region, resulting in a spaciotemporal gap in observation-based assessments of large-scale flooding patterns. We present new data from over 10 years of flood monitoring using a crest stage gauge network, combined with other channel monitoring records from multiple agency sources, to assess inter-decadal patterns of flood change in the area, with a specific emphasis on examining how various controls and disturbances have altered the character and seasonality of peak annual flows. Methods of analysis included the following: using Fisher's Exact Test to compare the seasonality of flooding between historic data spanning the 1970s and contemporary data obtained since 2010; summarizing GIS-based spatial data and meteorological timeseries to characterize study catchment conditions and changes between flood study periods; and relating spatiotemporal patterns of flood seasonality and occurrences of notably large floods with catchment characteristics and environmental changes. Our results show systematic patterns and changes in Flagstaff-area flood regimes that relate to geologic and topographic controls of the varied catchment systems, and in response to records of climate variations and local catchment disturbances, including urbanization and, especially, high-severity wildfire. For most catchments there has been a shift from predominantly late winter to spring snowmelt floods, or mixed seasonal flood regimes, towards monsoon-dominated flooding, patterns which may relate to observed local warming and precipitation changes. Post-wildfire flooding has produced extreme flood discharges which have likely exceeded historical estimates of flood magnitude over decade-long monitoring periods by one to two orders of magnitude. We advocate for continued monitoring and the expansion of local stream gauge networks to enable seasonal, magnitude-frequency trend analyses, improved climate and environmental change attribution, and to better inform the many planned and ongoing flood mitigation projects being undertaken in the increasingly developed Flagstaff region. [ABSTRACT FROM AUTHOR]

The 2021 heatwave over Western North America (WNA) led to record‐breaking air temperatures and human‐perceived heat stress (humidex) values. The event was accompanied by drier conditions driven by prolonged atmospheric blocking. During the heatwave, the maximum 6‐day means of humidex and temperature (HX‐6 and TX‐6) exhibited larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C), relative to 1981–2021 extended summer (June‐September) averages. Extreme indices of humidex show faster and larger increases than those of temperature, reflecting the nonlinear positive relationship between humidex and temperature. Future projections from a multi‐model ensemble of 19 Coupled Model Intercomparison Project Phase six (CMIP6) Global Climate Models (GCMs) clearly show an increase in humidex and temperature extremes, especially under intermediate and high emissions scenarios. Humidex indices (HX‐6 and HX95) show faster and larger increases than temperature indices (TX‐6 and TX95) for the same future years and global warming levels. Controlling for differences in GCM climate sensitivity to greenhouse gas forcing yields robust projections at various global warming levels, reducing the ranges of projected changes from the multi‐model ensemble. At 3.0°C global warming from pre‐industrial, the multi‐model ensemble projects occurrences of HX‐6, TX‐6, HX95, and TX95 over WNA that exceed 2021 levels to occur every 3.9, 1.7, 1.4, and 2.2 years, respectively, increasing to almost annually at 4.0°C. Plain Language Summary: In 2021 summer, Western North America (WNA) experienced an unprecedented heatwave, breaking air temperature and human‐perceived heat stress (humidex) records despite lower humidity due to prolonged atmospheric blocking. During the heatwave, consecutive 6‐day means of heat stress (HX‐6) and temperature (TX‐6) showed larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C) over the region, relative to their averages over the 1981–2010 summer period (June‐September). Heat stress increased faster and more significantly than air temperature due to their nonlinear relationship. Future projections from 19 climate models reveal clear increases in heat stress and temperature, especially under intermediate and high emission scenarios. Heat stress indices show larger and faster increases than temperature indices at the same future years and global warming levels. Accounting for uncertainty related to climate model sensitivity enhances projection reliability. At 3.0°C global warming from pre‐industrial levels, 2021‐level events over WNA are projected to occur every few years, becoming an almost annual occurrence at 4.0°C. The study underscores the critical need for robust projections using multi‐model ensembles. Key Points: Unprecedented 2021 summer heatwave over Western North America resulted in record‐breaking humidex and air temperature extremesLarger increases in humidex than temperature, exceeding their 2021 levels, under intermediate and high emissions scenariosRobust projections of multi‐model ensemble, mitigating climate sensitivity uncertainty of GCMs response to greenhouse gas emissions [ABSTRACT FROM AUTHOR]

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This study emulates dropsondes to elucidate the extent to which sporadic airborne sondes adequately represent divergence of moisture transport in Arctic atmospheric rivers (ARs). The convergence of vertically integrated moisture transport (IVT) plays a crucial role as it favours precipitation that significantly affects Arctic sea ice properties. Long-range research aircraft can transect ARs and drop sondes to determine their IVT divergence. In order to assess the representativeness of future sonde-based IVT divergence in Arctic ARs, we disentangle the sonde-based deviations from an ideal instantaneous IVT divergence, which result from undersampling by a limited number of sondes and from the flight duration. Our synthetic study uses C3S Arctic Regional Reanalysis (CARRA) reanalyses to set up an idealised scenario for airborne AR observations. For nine Arctic spring ARs, we mimic flights transecting each AR in CARRA and emulate sonde-based IVT representation by picking single vertical profiles. The emulation quantifies IVT divergence observability by two approaches. First, sonde-based IVT and its divergence are compared to the continuous IVT interpolated onto the flight cross-section. The comparison specifies uncertainties of discrete sonde-based IVT variability and divergence. Second, we determine how temporal AR evolution affects IVT divergence values by contrasting time-propagating sonde-based values with the divergence based on instantaneous snapshots. For our Arctic AR cross-sections, we find that coherent wind and moisture variabilities contribute less than 10 % to the total transport. Both quantities are uncorrelated to a great extent. Moisture turns out to be the more variable quantity. We show that sounding spacing greater than 100 km results in errors greater than 10 % of the total IVT along AR cross-sections. For IVT divergence, the Arctic ARs exhibit similar differences in moisture advection and mass convergence across the embedded front as mid-latitude ARs, but we identify moisture advection as being dominant. Overall, we confirm the observability of IVT divergence with an uncertainty of around 25 %–50 % using a sequence of at least seven sondes per cross-section. Rather than sonde undersampling, it is the temporal AR evolution over the flight duration that leads to high deviations in divergence components. In order to realise the estimation of IVT divergence from dropsondes, flight planning should consider not only the sonde positioning, but also the minimisation of the flight duration. Our benchmarks quantify sonde-based uncertainties as essential preparatory work for the upcoming airborne closure of the moisture budget in Arctic ARs. [ABSTRACT FROM AUTHOR]

The results of detailed numerical simulations of the Richtmyer–Meshkov instability of the interface between layers of air and either helium or SF6 in a shock tube are reported. Two- and three-dimensional simulations based on both the Euler and Navier–Stokes equations were obtained by a finite difference method that employs a front-tracking technique to keep the interface sharp. The nature of the flow patterns induced by the instability is discussed. The results of a numerical resolution study and a demonstration of the influence of boundary layers are presented also. Agreement with experimental data is found to be satisfactory, with the exception of the initial instability growth rate. [ABSTRACT FROM AUTHOR]

How does household exposure to a natural disaster affect higher education investments? Using variation in flooding from Hurricane Harvey (2017), we find that college-aged adults from flooded blocks in Houston are 7% less likely than counterparts to have student loans after Harvey, with larger effects in areas with more potential first-generation students. We find a similar relative decline in enrollment at more exposed Texas universities and colleges and a shift toward majors with higher expected earnings. Our results highlight a decrease in the quantity but an increase in the intensity of investments in human capital after the storm. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Volcanic aerosols reduce global mean precipitation in the years after major eruptions, yet the mechanisms that produce this response have not been rigorously identified. Volcanic aerosols alter the atmosphere's energy balance, with precipitation changes being one pathway by which the atmosphere acts to return toward equilibrium. By examining the atmosphere's energy budget in climate model simulations using radiative kernels, we explain the global precipitation reduction as largely a consequence of Earth's surface cooling in response to volcanic aerosols reflecting incoming sunlight. These aerosols also directly add energy to the atmosphere by absorbing outgoing longwave radiation, which is a major cause of precipitation decline in the first post‐eruption year. We additionally identify factors limiting the post‐eruption precipitation decline, and provide evidence that our results are robust across climate models. Plain Language Summary: Large volcanic eruptions can emit gas into the stratosphere that chemically forms sulfate aerosol particles. These aerosols persist in the stratosphere for up to 2 years, and have been linked to widespread precipitation changes in studies based on observations and models. Here we identify the mechanisms through which volcanic aerosols cause their clearest precipitation impact, a temporary reduction in global mean precipitation. Volcanic aerosols globally inhibit precipitation as a result of their abilities to both reflect incoming solar radiation and absorb longwave (terrestrial) radiation. The first is the more important influence on global precipitation, which is reduced for several years as an energetic response to cooler tropospheric temperatures. Absorption of longwave radiation further reduces precipitation during the first post‐eruption year, as a response to the increased energy brought into the stratosphere. Key Points: We identify the mechanisms of volcanic aerosol influence on global precipitation by assessing the atmospheric energy budget in simulationsPost‐eruption precipitation reduction is largely a consequence of a cooler surface, with latent heat balancing a less emissive troposphereVolcanic aerosols absorb outgoing longwave radiation into the stratosphere, and the added energy causes additional precipitation reduction [ABSTRACT FROM AUTHOR]

The effects of anthropogenic warming on the hydroclimate of California are becoming more pronounced with the increased frequency of multi-year droughts and flooding. As a past analog for the future, the Paleocene–Eocene Thermal Maximum (PETM) is a unique natural experiment for assessing global and regional hydroclimate sensitivity to greenhouse gas warming. Globally, extensive evidence (i.e., observations and climate models with high p CO 2) demonstrates hydrological intensification with significant variability from region to region (i.e., drier or wetter, greater frequency, and/or intensity of extreme events). Central California (paleolatitude ∼ 42° N), roughly at the boundary between dry subtropical highs and mid-latitude low-pressure systems, would have been particularly susceptible to shifts in atmospheric circulation and precipitation patterns/intensity. Here, we present new observations and climate model output on regional/local hydroclimate responses in central California during the PETM. Our findings, based on multi-proxy evidence within the context of model outputs, suggest a transition to an overall drier climate punctuated by increased precipitation during summer months along central coastal California during the PETM. [ABSTRACT FROM AUTHOR]

Based on the projected increase in hurricane landfall frequency on the middle to lower US east coast, we examined the crucial role of the estuarine wind field in exacerbating coastal flooding. A regionally refined atmospheric and two high-resolution hydrology and ocean models are integrated to provide plausible and physically consistent ensembles of hurricane events and the associated flooding inside the Delaware Bay and River, a US mid-Atlantic estuary. Model results show that the hurricane propagation direction, estuarine geometry, remote surge from the open ocean, and direct nearshore upwind stress could magnify the flood magnitude. More specifically, inland-bound tracks that make landfall before reaching the mid-Atlantic coast produce a more significant surge within Delaware Bay than the shore-parallel tracks, where the estuarine wind direction plays the primary role in surge amplification. Ultimately, this study emphasized the need for integrated models to capture the nonlinear dynamics and interactions in flood hazard modeling. [ABSTRACT FROM AUTHOR]

Heavy precipitation, which is changing significantly as Earth's climate warms, can result in flooding that seriously damages societies. However, little is known about how heavy precipitation of varying durations responds to the diverse gradients of urban development in China. Through statistical analyses spanning from 1990 to 2021, we have examined shorter‐duration (≤3 days) and longer‐duration (>3 days) heavy precipitation across a spectrum of urban development, encompassing long‐term built‐up (LTB), recently built‐up (RTB), and rural background catchments within each urban agglomeration catchment (UAC) across China. We find that urbanization primarily influences shorter‐duration heavy precipitation, with a more pronounced effect observed in the LTB catchments. Conversely, the influence of urbanization on longer‐duration heavy precipitation appears to be more weakened in the RTB catchments. The intensification of shorter‐duration heavy precipitation induced by urbanization is more pronounced in humid regions and within larger UACs, while the urban effect on longer‐duration heavy precipitation is weaker in humid regions and within larger UACs. Notably, the attribution analysis results of the geographical detector model confirm our findings. Anthropogenic‐related factors (population density, nighttime light data, impervious surface percent, land surface temperature) significantly influence shorter‐duration heavy precipitation in more UACs than natural factors (distance from the coast, wind and elevation), while natural factors dominate longer‐duration events in larger UACs across China. Our results highlight the necessity of considering the spatial difference between the UAC center and UAC periphery for accurate projections and effective prevention of heavy precipitation and potential flood risks in the future. Plain Language Summary: Heavy precipitation can lead to severe flooding, causing substantial damage to communities. However, understanding how heavy precipitation, varying in duration, responds to different urban development scenarios in China remains limited. We conduct a thorough analysis from 1990 to 2021, exploring shorter‐duration (up to 3 days) and longer‐duration (over 3 days) heavy rainfall across a range of urban settings. Urbanization notably impacts shorter‐duration heavy rainfall, particularly in long‐term built‐up areas, and its influence weakens in recently built‐up regions for longer‐duration heavy rainfall. The amplification of shorter‐duration heavy rainfall due to urbanization is more significant in humid regions and larger urban areas, while the impact on longer‐duration heavy rainfall is less pronounced in humid regions and larger urban areas. Our findings emphasize the need to account for spatial differences within urban agglomerations for accurate projections and effective prevention of heavy rainfall and potential flood risks in the future. Key Points: A catchment‐based strategy is proposed to analyze the heavy precipitation changes in response to urbanizationUrbanization tends to increase shorter‐duration heavy precipitation but has a weaker effect on longer‐duration heavy precipitationAnthropogenic‐related factors drive shorter‐duration heavy precipitation in more UACs, while natural factors dominate longer‐duration events [ABSTRACT FROM AUTHOR]

High‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally. Plain Language Summary: The STAR‐ESDM tool is able to quickly and accurately generate future climate projections for weather stations and high‐resolution grids anywhere in the world. It does this by breaking down global or regional climate model output into different components, from the long‐term trend to the day‐to‐day variability, then merging projected changes with observations. When tested against projections generated by a complex and computationally expensive dynamical global model, STAR‐ESDM produced almost the same output, even for extreme temperature and precipitation values, at a fraction of the computational cost. Moreover, unlike most statistical downscaling models, this method isn't tied to any specific geographic area or predictand and/or predictor data set. It can be applied to any regional or global data set, whether generated by a climate or reanalysis model, derived from satellite observations, recorded at weather stations, and more. As climate impacts escalate, STAR‐ESDM offers a flexible and effective way to generate the high‐resolution climate projections needed to better gauge climate risk and enhance resilience anywhere in the world where reliable observational or quasi‐observational data, including reanalysis or satellites, are available. This is particularly relevant to under‐resourced regions, which are often most vulnerable to climate impacts as well as most lacking in future projections. Key Points: STAR‐ESDM is a rapid, flexible and generalizable approach to bias‐correct and downscale climate model output to any finer‐resolution data setPredictors/predictands can be derived from global or regional models, satellites, reanalysis, gridded observations and weather stationsProjected changes in temperature and precipitation mirror those of a high‐resolution global model at a fraction of the computational cost [ABSTRACT FROM AUTHOR]

This paper describes the addition of a stratospheric prognostic aerosol (SPA) capability – developed with the goal of accurately simulating sulfate aerosol formation and evolution in the stratosphere – in the Department of Energy (DOE) Energy Exascale Earth System Model, version 2 (E3SMv2). The implementation includes changes to the four-mode Modal Aerosol Module microphysics in the stratosphere to allow for larger particle growth and more accurate stratospheric aerosol lifetime following the Pinatubo eruption. E3SMv2-SPA reasonably reproduces stratospheric aerosol lifetime, burden, aerosol optical depth, and top-of-atmosphere flux when compared to remote sensing observations. E3SMv2-SPA also has close agreement with the interactive chemistry–climate model CESM2-WACCM (Community Earth System Model version 2–Whole Atmosphere Community Climate Model) – which has a more complete chemical treatment – and the observationally constrained, prescribed volcanic aerosol treatment in E3SMv2. Global stratospheric aerosol size distributions identify the nucleation and growth of sulfate aerosol from volcanically injected SO 2 from both major and minor volcanic eruptions from 1991 to 1993. The modeled aerosol effective radius is consistently lower than satellite and in situ measurements (max differences of ∼ 30 %). Comparisons with in situ size distribution samples indicate that this simulated underestimation in both E3SMv2-SPA and CESM2-WACCM is due to overly small accumulation and coarse-mode aerosols 6–18 months post-eruption, with E3SMv2-SPA simulating ∼ 50 % of the coarse-mode geometric mean diameters of observations 11 months post-eruption. Effective radii from the models and observations are used to calculate offline scattering and absorption efficiencies to explore the implications of smaller simulated aerosol size for the Pinatubo climate impacts. Scattering efficiencies at wavelengths of peak solar irradiance (∼ 0.5 µ m) are 10 %–80 % higher for daily samples in models relative to observations through 1993, suggesting higher diffuse radiation at the surface and a larger cooling effect in the models due to the smaller simulated aerosol; absorption efficiencies at the peak wavelengths of outgoing terrestrial radiation (∼ 10 µ m) are 15 %–40 % lower for daily samples in models relative to observations, suggesting an underestimation in stratospheric heating in the models due to the smaller simulated aerosol. These potential biases are based on aerosol size alone and do not take into account differences in the aerosol number. The overall agreement of E3SMv2-SPA with observations and its similar performance to the well-validated CESM2-WACCM makes E3SMv2-SPA a viable alternative to simulating climate impacts from stratospheric sulfate aerosols. [ABSTRACT FROM AUTHOR]

Understanding mesocarnivore responses to both natural and anthropogenic disturbance is crucial for understanding species' potential to maintain landscape persistence into the future. We examined the response of five mesocarnivore species (bobcat, coyote, fisher, gray fox, and red fox) to both types of disturbances and climatic conditions. The Northeastern U.S. has experienced multiple large‐scale disturbances, such as a mass defoliation event following larval spongy moth outbreak and high densities of infrastructure that divide the natural landcover into roadless zones where these species inhabit. Using dynamic occupancy models in a Bayesian framework, we aimed to (1) examine variation in species' responses over a 4‐year study by estimating variation in site‐level occupancy, colonization and extirpation of each species in the state of Rhode Island relative to natural disturbance (i.e., defoliation event), anthropogenic disturbance (i.e., parceling of natural landcover bounded by roads, distance to roads), and climate (i.e., seasonal precipitation) and (2) compare current occurrence trends to predicted asymptotic occupancy to identify key variables contributing to distribution instability. Our findings indicated declines in the occurrence of both fox species, and fisher. There was variation in mesocarnivore response to disturbance among the species. We found gray fox and fisher occupancy dynamics to be sensitive to all forms of disturbance and coyote occurrence was positively associated with anthropogenic disturbance. Although bobcat and red fox were predicted to respond positively to future climate scenarios, fisher and gray fox were not, and persistence of fisher and gray fox in a landscape of disturbance relies on large areas with high forest and shrubland cover. With the wide‐spread spongy moth outbreak across much of southern New England, our findings indicate that efforts to conserve forested lands may be crucial in maintaining the persistence of several mesocarnivore species in this region experiencing large‐scale disturbance. [ABSTRACT FROM AUTHOR]

Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea‐surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100‐km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large‐scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14‐km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28‐km regional refinement and uniform 111‐km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4‐standard‐deviation anomaly in the seasonal‐mean NAO for 2°C SST anomalies. The lower‐resolution simulations show a weaker response with a different spatial structure. The enhanced large‐scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient‐eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher‐resolution models and that regional refinement in key regions offers a potential pathway to improve multi‐year regional climate predictions based on midlatitude SSTs. Plain Language Summary: Variations in the ocean surface temperature (SST) influence the atmospheric circulation and thus climate over land. Canonical understanding is that tropical SSTs are more important than SSTs in midlatitudes. However, this understanding is based on climate models that don't resolve processes at scales less than 100 km. Here, we show that by increasing the atmospheric model resolution to resolve features on smaller scales, such as weather fronts, we find a larger atmospheric circulation response to midlatitude SST anomalies in the North Atlantic. North Atlantic SST anomalies can be predicted multiple years in advance, and a larger atmospheric circulation response to these predictable SST anomalies therefore implies increased predictability of climate over the surrounding land regions. Key Points: There is a large circulation response to idealized Gulf Stream sea‐surface temperature (SST) anomalies in an atmospheric model with 14‐km regional grid refinementThis response is weaker or absent in simulations with 28‐km or coarser resolution, which do not fully resolve mesoscale frontal processesTransient‐eddy fluxes of heat and momentum are modified as fronts pass over warm SSTs, leading to a large‐scale circulation response [ABSTRACT FROM AUTHOR]

We examine tropical rainfall from the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4 (GFDL AM4) at three horizontal resolutions of 100 km, 50 km, and 25 km. The model produces more intense rainfall at finer resolutions, but a large discrepancy still exists between the simulated and the observed frequency distribution. We use a theoretical precipitation scaling diagnostic to examine the frequency distribution of the simulated rainfall. The scaling accurately produces the frequency distribution at moderate‐to‐high intensity (≥10 mm day−1). Intense tropical rainfall at finer resolutions is produced primarily from the increased contribution of resolved precipitation and enhanced updrafts. The model becomes more sensitive to the grid‐scale updrafts than local thermodynamics at high rain rates as the contribution from the resolved precipitation increases. Plain Language Summary: State of the art global scale climate models have horizontal resolutions of the order of tens of kilometers. However, these resolutions are much lower than the scales required to resolve tropical convection. This study investigates whether a resolution increase from 100 km to 25 km results in notable improvements in simulating tropical rainfall. Higher resolution simulations capture more intense rainfall events that are missed by their coarser counterparts. However, they struggle to capture the accurate frequency distribution of intense rainfall events. Results reported in this study underscore the importance of scrutinizing and carefully interpreting the outcomes of high‐resolution climate model simulations. Key Points: Tropical rainfall distribution is analyzed using simulations from the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4 at three horizontal resolutions: 100 km, 50 km, and 25 kmIncreasing horizontal resolution yields more intense tropical rainfall but not the accurate frequency distributionTheoretical precipitation scaling accurately captures the frequency distribution of the simulated precipitation at moderate‐to‐high intensity [ABSTRACT FROM AUTHOR]

Climate models robustly project acceleration of the Brewer‐Dobson circulation (BDC) in response to climate change. However, the BDC trends simulated by comprehensive models are poorly constrained by observations, which cannot even determine the sign of potential trends. Additionally, the changing structure of the troposphere and stratosphere has received increasing attention in recent years. The extent to which vertical shifts of the circulation are driving the acceleration is under debate. In this study, we present a novel method that enables the attribution of advective BDC changes to structural changes of the circulation and of the stratosphere itself. Using this method allows studying the advective BDC trends in unprecedented detail and sheds new light into discrepancies between different data sets (reanalyses and models) at the tropopause and in the lower stratosphere. Our findings provide insights into the reliability of model projections of BDC changes and offer new possibilities for observational constraints. Plain Language Summary: The large‐scale interhemispheric meridional overturning circulation in the middle atmosphere determines the composition of this region, including the distribution of radiatively important trace gases. The long‐term change of this circulation is a subject of ongoing debate, and an area of disagreement between models and observations. In our study we present a method that provides an unprecedented insight into the change and disentangles the individual factors behind it. Hence, the method introduces new constraints on the circulation change and can aid the reconciliation between models and observations. Key Points: A method for attributing net upwelling trends to changes in circulation, air density and structure of the upwelling region is establishedModels agree largely on contributions from vertical shifts in pressure surfaces/tropopause and changes in vertical advectionFor reanalyses, on the other hand, the uncertainty is larger, not allowing direct constraints on model trends [ABSTRACT FROM AUTHOR]

The mesoscale dynamics of a record‐breaking Atmospheric River (AR) that impacted the Middle East in mid‐April 2023 and caused property damage and loss of life are investigated using model, reanalysis and observational data. The high‐resolution (2.5 km) simulations revealed the presence of AR rapids, narrow and long convective structures embedded within the AR that generated heavy precipitation (>4 mm hr−1) as they moved at high speeds (>30 m s−1) from northeastern Africa into western Iran. Gravity waves triggered by the complex terrain in Saudi Arabia further intensified their effects. Given the rising frequency of ARs in this region, AR rapids may be even more impactful in a warming climate, and need to be accounted for in reanalysis and numerical models. Plain Language Summary: Atmospheric Rivers (ARs) are narrow and long bands of high water vapor content, which largely originate in the tropics or subtropics and propagate into mid‐ and high‐latitudes. They can bring beneficial rain and snow but, in particular the most intense, can lead to catastrophic flooding and loss of life. One of such occurrences in the Middle East in mid‐April 2023 is investigated using model and observational data. The high‐resolution (2.5 km) simulation put in evidence narrow (5–15 km) and long (100–200 km) convective structures within the AR, known as AR rapids, which produced heavy precipitation (>4 mm hr−1), further enhanced by gravity waves that developed over the high terrain in western Saudi Arabia, and propagated at high speeds (>30 m s−1). ARs are occurring more frequently in the Middle East as they are globally, and with increased atmospheric water vapor in a warming climate, AR rapids may be even more destructive. Key Points: The exceptional and impactful Atmospheric River of mid‐April 2023 in the Middle East is investigated with model and observational dataA 2.5 km simulation reveals the presence of "Atmospheric River rapids," narrow along‐flow structures that generated rain rates >4 mm hr−1Model simulations and ground‐based observations underlined the effects of the Atmospheric River and associated trailing cold front [ABSTRACT FROM AUTHOR]

The tropical tropopause layer (TTL) is the main gateway for air transiting from the troposphere to the stratosphere and therefore impacts the chemical composition of the stratosphere. In particular, the cold-point tropopause, where air parcels encounter their final dehydration, effectively controls the water vapor content of the lower stratosphere. Given the important role of stratospheric water vapor for the global energy budget, it is crucial to understand the long-term changes in cold-point temperature and their impact on water vapor trends. Our study uses Global Navigation Satellite System – Radio Occultation (GNSS-RO) data to show that there has been no overall cooling trend of the TTL over the past 2 decades, in contrast to observations prior to 2000. Instead, the cold point is warming, with the strongest trends of up to 0.7 K per decade during boreal winter and spring. The cold-point warming shows longitudinal asymmetries, with the smallest warming over the central Pacific and the largest warming over the Atlantic. These asymmetries are anticorrelated with patterns of tropospheric temperature trends, and regions of strongest cold-point warming are found to show slight cooling trends in the upper troposphere. Overall, the here-identified warming of the cold point is consistent with model predictions under global climate change, which attribute the warming trends to radiative effects. The seasonal signals and zonal asymmetries of the cold-point temperature and height trends might be related to dynamical responses to enhanced upper-tropospheric heating, changing convection, or trends in the stratospheric circulation. Water vapor observations in the TTL show mostly positive trends consistent with cold-point warming for 2004–2021. We find a decrease in the amplitude of the cold-point temperature seasonal cycle by ∼ 7 % driving a reduction in the seasonal cycle in 100 hPa water vapor by 5 %–6 %. Our analysis shows that this reduction in the seasonal cycle is transported upwards together with the seasonal anomalies and has reduced the amplitude of the well-known tape recorder over the last 2 decades. [ABSTRACT FROM AUTHOR]

The exponential ascension of artificial intelligence (AI) prompts profound inquiries concerning equitable access to its advantages versus environmental externalities. While trailblazing economies relish AI’s benefits such as economic expansion and technological eminence, the colossal energy required to train and operate AI systems exacts a hefty toll on the environment, disproportionately burdening marginalized nations. This imbalanced paradigm epitomizes disparities of the digital divide, with impoverished nations bearing externalities while lacking access to innovations. This study aims to explore the intricate relationship between AI and environmental sustainability through a qualitative methodology encompassing a literature review and document analysis of industry practices and viewpoints. The findings unveil AI as a double-edged sword, with empirical analyses exposing its striking carbon emissions and resource depletion, which if left unchecked, could impede global decarbonization initiatives. However, AI also demonstrates strong potential for optimizing energy systems, predictive modelling, and advancing climate solutions if conscientiously developed. The study elucidates this conundrum and proposes responsible innovation pathways involving renewable energy adoption, enhanced efficiency, optimized hardware, carbon accounting, transparency, and legislative mindfulness. Integrating climate justice and digital divide perspectives illuminates avenues for steering AI’s trajectory towards environmental stewardship and inclusive accessibility through proactive collaboration across sectors. Ultimately, collective wisdom will determine whether AI ushers in climate justice or injustice. [ABSTRACT FROM AUTHOR]

Climate change is increasing the frequency and intensity of extreme precipitation events, raising the risk of urban flood disasters. This study uses a crowd‐sourced municipal call database to characterize the spatial distribution of flood risk in Detroit, MI. Call data including dates and addresses were obtained from the City of Detroit Department of Public Works for 2021. Calls were mapped and aggregated to census tract counts and merged with neighborhood‐level data. Associations of predictors with flood calls were tested using spatial regression models. Flooding calls were located throughout the city but were concentrated in specific areas. Multivariate models of census tract level call counts indicated that increased poverty and Black, immigrant, and older residents were positively associated with flood calls, while increased elevation was associated with protective effects. Longer distances from waste water interceptors were associated with higher risk for calls. Crowd‐sourced flood hotline call data can be used for effective spatial flood risk assessment. Though flooding occurs throughout the city of Detroit, infrastructural, neighborhood, and household factors influence flooding extent. Limitations included the self‐reported nature of calls. Future modeling efforts might include input from local stakeholders to improve spatial risk assessment. [ABSTRACT FROM AUTHOR]

Lake‐effect snow (LES) storms, characterized by heavy convective precipitation downwind of large lakes, pose significant coastal hazards with severe socioeconomic consequences in vulnerable areas. In this study, we investigate how devastating LES storms could evolve in the future by employing a storyline approach, using the LES storm that occurred over Buffalo, New York, in November 2022 as an example. Using a Pseudo‐Global Warming method with a fully three‐dimensional two‐way coupled lake‐land‐atmosphere modeling system at a cloud‐resolving 4 km resolution, we show a 14% increase in storm precipitation under the end‐century warming. This increase in precipitation is accompanied by a transition in the precipitation form from predominantly snowfall to nearly equal parts snowfall and rainfall. Through additional simulations with isolated atmospheric and lake warming, we discerned that the warmer lake contributes to increased storm precipitation through enhanced evaporation while the warmer atmosphere contributes to the increase in the storm's rainfall, at the expense of snowfall. More importantly, this shift from snowfall to rainfall was found to nearly double the area experiencing another winter hazard, Rain‐on‐Snow. Our study provides a plausible future storyline for the Buffalo LES storm, focusing on understanding the intricate interplay between atmospheric and lake warming in shaping the future dynamics of LES storms. It emphasizes the importance of accurately capturing the changing lake‐atmosphere dynamics during LES storms under future warming. Plain Language Summary: A lake‐effect snow (LES) storm is a heavy snowfall that occurs when a cold dry air mass travels over a relatively warm lake. LES storms have been particularly known to decimate regions downwind of the North American Great Lakes with several meters of snow and millions of dollars in damages. In this study, we use a fully 3D comprehensive lake‐land‐atmosphere modeling system, along with the Pseudo‐Global Warming technique that incorporates large‐scale climate changes into our simulation, to investigate how such devastating LES storms could unfold in a warming climate. We used the LES storm that hit Buffalo, New York in November 2022 as a case study. Our results revealed that under a warmer future climate, LES storms can produce a higher precipitation amount by up to 14%. More notably, our results showed a transition in the form of storm precipitation from mostly snowfall to nearly equal parts snowfall and rainfall. The warmer lake in the future was found to be the primary reason behind the increase in precipitation through enhanced evaporation from the lake. The warmer atmosphere in the future explained why rainfall replaced snowfall in the LES storm. Key Points: Future of lake‐effect snow storms is explored using Pseudo‐Global Warming with a 3D two‐way coupled lake‐land‐atmosphere modeling systemFuture climate amplifies storm precipitation via higher lake evaporation and boosts rainfall at snowfall's expense via a warmer atmosphereLake evaporation plays a critical role in storm precipitation amount and accurately resolving the future lake‐atmosphere dynamics is vital [ABSTRACT FROM AUTHOR]

Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance. Plain Language Summary: As global temperatures rise, there's growing concern about an increase in the frequency of extreme precipitation events. However, projections of future changes to large‐magnitude precipitation events often rely on coarse‐resolution models which simulate meteorology on a spatial scale larger than most storms. To address this, we explored the potential of higher resolution models from the Coupled Model Intercomparison Project HighResMIP ensemble to improve the fidelity of simulated precipitation variability across the western United States. We compared four HighResMIP models with their coarse‐resolution counterparts from 1950 to 2014 and found that coarse‐resolution models consistently underestimated the dominance of large storms compared to observed data. While some HighResMIP models showed improvement, others remained unchanged or even showed increased bias. This suggests that besides spatial resolution alone, factors like model structure and numerical representation of fine scale precipitation processes play a role in accurately simulating intense storms. These findings highlight the importance of considering both resolution and climate model structure in predicting future extreme precipitation events, especially in regions where precipitation is generated by processes that operate on a wide range of spatial scales. Key Points: Increasing spatial resolution of general circulation models (GCMs) does not consistently improve simulation of large‐storm dominance across the western United StatesCoarse resolution GCMs simulate large‐storm dominance well in regions where the largest daily precipitation events are due to synoptic scale precipitationModel structure and parameterization of precipitation generation appear to be equally as important as resolution in the simulation of large‐storm dominance [ABSTRACT FROM AUTHOR]

Climate change is projected to worsen ozone pollution over many populated regions, with larger impacts at higher concentrations. More intense and frequent ozone episodes risk setbacks to human health and environmental policy achievements. However, assessing these changes is complicated by uncertain climate sensitivity, closely related to climate model response, and internal variability in simulations projecting climate's influence on air quality. Here, leveraging a global modeling framework that one‐way couples a human activity model, an Earth system model of intermediate complexity, and an atmospheric chemistry model, we investigate the role of climate sensitivity in climate‐induced changes to high ozone pollution episodes in the United States using multiple greenhouse gas emissions scenarios, representations of climate sensitivity, and initial condition members. We bias correct and evaluate historical model simulations, identifying modeled and observed O3 episodes using extreme value theory, and extend the approach to projections of mid‐ and end‐century climate impacts. Results show that the influence of climate sensitivity can be as significant as that of greenhouse gas emissions scenario absent precursor emissions changes. Climate change is projected to increase the magnitude of the highest annually occurring O3 concentrations by over 2.3 ppb on average across the U.S. at mid‐century under a high climate sensitivity and moderate emissions scenario, but the increase is limited to less than 0.3 ppb under lower climate sensitivity. Further, we show that areas in the U.S. currently meeting air quality standards risk being pushed into non‐compliance due to a climate‐induced increase in frequency of high ozone days. Plain Language Summary: Climate change can worsen O3 air pollution across much of the U.S., threatening environmental policy goals and posing health risks. Projections of this impact rely on models that have uncertainties associated with simulating future climate. We investigated the influence of uncertainty from climate sensitivity, or the simulated change in Earth's average surface temperature in response to an increase in atmospheric CO2, on projections of air quality using a collection of simulations. To improve O3 estimates based on model simulations, we applied a new bias correction method to the model concentrations that accounts for spatial differences in atmospheric pollution. Then, we applied extreme value statistics to describe the behavior of the highest O3 concentrations. We found that, under climate change, the most polluted days worsen and occur more frequently, making it more difficult to meet air quality standards in highly populated regions and suggesting that continued efforts to mitigate pollutant emissions are necessary to counteract climate change's effects on O3. Further, our results indicate that the model's climate sensitivity is as influential as greenhouse gas emissions scenario for mid‐century projections, showing that the strength of the earth system's response to greenhouse gas levels is a critical consideration when simulating climate impacts. Key Points: We project climate impacts on US ozone episodes with spatially varying bias correction and a climate and air quality simulation ensembleClimate sensitivity can be as influential as GHG scenario without air pollutant emissions changes in mid‐century ozone episodesClimate‐induced increases in ozone pollution may increase risk of not meeting air quality standards in several areas of the U.S. [ABSTRACT FROM AUTHOR]

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the "signal-tonoise paradox" and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media. [ABSTRACT FROM AUTHOR]

This study presents an updated and homogenised ozone time series covering 34 years (1987–2020) of ozonesonde measurements at Lauder, New Zealand, and attributes vertically resolved ozone trends using a multiple linear regression (MLR) analysis and a chemistry–climate model (CCM). Homogenisation of the time series leads to a marked difference in ozone values before 1997, in which the ozone trends are predominantly negative from the surface to ∼ 30 km, ranging from ∼ - 2 % per decade to - 13 % per decade, maximising at around 12–13 km, in contrast to the uncorrected time series which shows no clear trends for this period. For the post-2000 period, ozone at Lauder shows negative trends in the stratosphere, maximising just below 20 km (∼ - 5 % per decade) despite the fact that stratospheric chlorine and bromine from ozone-depleting substances (ODSs) have both been declining since 1997. However, the ozone trends change from negative for 1987–1999 to positive in the post-2000 period in the free troposphere. The post-2000 ozone trends calculated from the ozonesonde measurements compare well with those derived from the co-located low-vertical-resolution Fourier-transform infrared spectroscopy (FTIR) ozone time series. The MLR analysis identifies that the increasing tropopause height, associated with CO2 -driven dynamical changes, is the leading factor driving the continuous negative trend in lower-stratospheric ozone at Lauder over the whole observational period, whilst the ozone-depleting substances (ODSs) only contribute to the negative ozone trend in the lower stratosphere over the pre-1999 period. Meanwhile, stratospheric temperature changes contribute significantly to the negative ozone trend above 20 km over the post-2000 period. Furthermore, the chemistry–climate model (CCM) simulations that separate the effects of individual forcings show that the predominantly negative modelled trend in ozone for the 1987–1999 period is driven not only by ODSs but also by increases in greenhouse gases (GHGs), with large but opposing impacts from methane (positive) and CO2 (negative), respectively. Over the 2000–2020 period, the model results show that the CO2 increase is the dominant driver for the negative trend in the lower stratosphere, in agreement with the MLR analysis. Although the model underestimates the observed negative ozone trend in the lower stratosphere for both periods, it clearly shows that CO2 -driven dynamical changes have played an increasingly important role in driving the lower-stratospheric ozone trends in the vicinity of Lauder. [ABSTRACT FROM AUTHOR]

Downbursts winds, characterized by strong, localized downdrafts and subsequent horizontal straight-line winds, present a significant risk to civil structures. The transient nature and limited spatial extent present measurement challenges, necessitating analytical models for an accurate understanding and predicting their action on structures. This study analyzes the Sânnicolau Mare downburst event in Romania, on 25 June 2021, using a bi-dimensional analytical model coupled with the teaching–learning-based optimization (TLBO) algorithm. The intent is to understand the distinct solutions generated by the optimization algorithm and assess their physical validity. Supporting this examination are a damage survey and wind speed data recorded during the downburst event. Employed techniques include agglomerative hierarchical K -means clustering (AHK-MC) and principal component analysis (PCA) to categorize and interpret the solutions. Three main clusters emerge, each displaying different storm characteristics. Comparing the simulated maximum velocity with hail damage trajectories indicates that the optimal solution offers the best overlap, affirming its effectiveness in reconstructing downburst wind fields. However, these findings are specific to the Sânnicolau Mare event, underlining the need for a similar examination of multiple downburst events for broader validity. [ABSTRACT FROM AUTHOR]

Tropical Cyclones (TCs) inflict substantial coastal damages, making it pertinent to understand changing storm characteristics in the important nearshore region. Past work examined several aspects of TCs relevant for impacts in coastal regions. However, few studies explored nearshore storm intensification and its response to climate change at the global scale. Here, we address this using a suite of observations and numerical model simulations. Over the historical period 1979–2020, observations reveal a global mean TC intensification rate increase of about 3 kt per 24‐hr in regions close to the coast. Analysis of the observed large‐scale environment shows that stronger decreases in vertical wind shear and larger increases in relative humidity relative to the open oceans are responsible. Further, high‐resolution climate model simulations suggest that nearshore TC intensification will continue to rise under global warming. Idealized numerical experiments with an intermediate complexity model reveal that decreasing shear near coastlines, driven by amplified warming in the upper troposphere and changes in heating patterns, is the major pathway for these projected increases in nearshore TC intensification. Plain Language Summary: Tropical cyclones (TCs) that intensify close to the coast pose a major socio‐economic threat and are a substantial challenge from an operational standpoint. Therefore understanding historical trends in nearshore storm intensification and how they may change in future is of considerable significance. Despite this, few studies examined this key aspect of TCs at the global scale. Here we show, using an analysis of observations and atmospheric reanalyses, that the mean TC intensification rate has increased significantly over the period 1979–2020 primarily aided by increases in relative humidity and decreases in vertical wind shear. Further, high‐resolution climate models, which explicitly resolve TCs, suggest that nearshore TC intensification will continue to increase in future. These increases in coastal TC intensification rates can mainly be attributed to stronger projected decreases in vertical wind shear. To better understand wind shear projections, a suite of idealized numerical experiments with an intermediate complexity model were conducted. The experiments indicate that enhanced warming in the upper‐troposphere and changing heating patterns are likely responsible. Key Points: Tropical cyclone (TC) intensification rates have increased in near coastal regions over the 42‐year period 1979‐2020Increases in relative humidity along with decreases in vertical wind shear are responsibleClimate models project a continued increase in nearshore TC intensification rates with decreasing wind shear playing a crucial role [ABSTRACT FROM AUTHOR]