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Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

The number and coverage of weather observations over the oceans were considerably reduced during World War II (WW2) due to disruptions to normal trade routes. The observations that do exist for this period are often unavailable to science as they are still only available as paper records or scanned images. We have rescued the detailed hourly weather observations contained in more than 28,000 logbook images of the US Navy Pacific Fleet stationed at Hawai'i during 1941–1945 to produce a dataset of more than 630,000 records. Each record contains the date and time, positional information and several meteorological measurements, totalling more than 3 million individual observations. The data rescue process consisted of a citizen science project asking volunteers to transcribe the observations from the available images, followed by additional quality control processes. This dataset not only contains hourly weather observations of air temperature, sea surface temperature, atmospheric pressure, wind speed and wind direction, mainly in the Pacific Ocean but also includes some observations from the Atlantic and Indian Oceans. The new observations are found to be of good quality by inter‐comparing independent measurements taken on ships travelling in convoy and by comparing with the 20th‐Century Reanalysis. This dataset provides invaluable instrumental weather observations at times and places during WW2, which fill gaps in existing reconstructions. [ABSTRACT FROM AUTHOR]

This paper presents the first quantitative relationship between the cold point tropopause (CPT) and tropical easterly jet (TEJ) using radiosonde observations over Gadanki (13.45° N, 79.2° E) during the Indian summer monsoon season 2006–2014. CPT and TEJ peak altitudes (HCPTandHTEJ) show amalgams of two categories of variability on the day‐to‐day scale. In category1 HTEJ occurs close to HCPT and they show in‐phase variation. While in Category2 HTEJ occurs far apart from HCPT and they do not show any relationship. For Category1 HCPT and HTEJ are strongly correlated (0.70), as well as HCPT and TCPT (CPT temperature) are moderately anticorrelated (−0.55) significant at a 95% confidence level, indicating the dominance of adiabatic processes. Whereas in Category2 HCPT and TCPT are not significantly anti‐correlated. Thus, when TEJ and CPT are close to each other, it may serve as an indicator for the prevalence of the synoptic‐scale effect. [ABSTRACT FROM AUTHOR]

This paper offers essential insights into Southeast Asiaʼs transition to clean energy, a cornerstone for global climate objectives. Based on 27 interviews with regional energy and climate experts conducted between September 2022 and October 2023, the research distils key factors into 3Ds: Demanding, Doable, and Dependent. Highlighting these aspects would foster readiness, persuade stakeholders, and secure international support, all of which are pivotal for advancing the energy transition towards net‐zero emissions in Southeast Asia. [ABSTRACT FROM AUTHOR]

There is a growing recognition of the importance of research into the effects of climate change on mental health and wellbeing. This paper provides an overview of the pathways through which climate change can affect mental health and wellbeing, highlighting the valuable contribution that health geography can make in this field of study. Given expertise in spatial processes, human‐environment interactions, and diverse research methods, health geographers are well‐equipped to enhance our understanding of the connection between climate change and mental health and wellbeing. The paper proposes two key areas of future focus: (1) exploring the reciprocal relationships between mental health and place, and (2) integrating knowledge from health geography and environmental sustainability. Health geography can play a critical role in developing knowledge to support mitigation strategies and promote mental health and wellbeing in the face of climate change. [ABSTRACT FROM AUTHOR]

Environmental conditions significantly affected development in the Asian tropics. This paper investigates the relationship between weather risk and agriculture in four regions with distinct climatological features. Using new data, we estimate the scale of crop output sensitivity to rainfall shocks across ecological zones. Output was sensitive to shocks in regions with low levels, concentrated spells and high volatility of rainfall. Canal irrigation protected some districts while unirrigated regions remained vulnerable. Regions with high rainfall levels and long seasons remained protected. Regions with large interruptions deterred investment and were underdeveloped while regions with small interruptions invited investment‐led growth. [ABSTRACT FROM AUTHOR]

The cloud properties and governing processes in Southern Ocean marine boundary layer clouds have emerged as a central issue in understanding the Earth's climate sensitivity. While our understanding of Southern Ocean cloud feedbacks have evolved in the most recent climate model intercomparison, the background properties of simulated summertime clouds in the Southern Ocean are not consistent with measurements due to known biases in simulating cloud condensation nuclei concentrations. This paper presents several case studies collected during the Capricorn 2 and Marcus campaigns held aboard Australian research vessels in the Austral Summer of 2018. Combining the surface‐observed cases with MODIS data along forward and backward air mass trajectories, we demonstrate the evolution of cloud properties with time. These cases are consistent with multi‐year statistics showing that long trajectories of air masses over the Antarctic ice sheet are critical to creating high droplet number clouds in the high latitude summer Southern Ocean. We speculate that secondary aerosol production via the oxidation of biogenically derived aerosol precursor gasses over the high actinic flux region of the high latitude ice sheets is fundamental to maintaining relatively high droplet numbers in Southern Ocean clouds during Summer. Plain Language Summary: The amount of warming the Earth will experience because of increasing carbon dioxide levels in the atmosphere is sensitive to the properties of clouds that occur over the Southern Ocean. The atmosphere over the circumpolar Southern Ocean is poorly understood and presents significant challenges to climate models. Here we document the properties of the ubiquitous Southern Ocean low‐level clouds that exert a strong influence on the albedo of this region. We find that high cloud droplet number concentrations are associated with air masses that have taken paths over the high‐altitude ice sheets. The chemistry of the aerosol on which the cloud droplets form suggests that aerosols that have recently condensed from gasses emitted by phytoplankton in the highly productive waters near Antarctica are an important component of the cloud properties that must be correctly simulated in models. Key Points: High cloud droplet number concentrations are associated with air masses that have recently passed over continental AntarcticaCloud droplet number concentrations decrease with time as clouds evolve in over‐water trajectories due to scavenging by precipitationKatabatic flows bring high concentrations of cloud condensation nuclei to the marine boundary layer where they influence cloud properties [ABSTRACT FROM AUTHOR]

Populations and ecological communities are changing worldwide, and empirical studies exhibit a mixture of either declining or mixed trends. Confusion in global biodiversity trends thus remains, while assessing such changes is of major social, political, and scientific importance. Part of this variability may arise from the difficulty to reliably assess global biodiversity trends. Here, we conducted a literature review of studies documenting the temporal dynamics of global biodiversity. We classified the differences among approaches, data, and methodology used by the reviewed papers to reveal common findings and sources of discrepancies. We show that reviews and meta‐analyses, along with the use of global indicators, are more likely to conclude that trends are declining. On the other hand, the longer the data are available, the more nuanced are the trends they generate. Our results also highlight the lack of studies providing information on the impact of synergistic pressures on a global scale, making it even more difficult to understand the driving factors of the observed changes and how to decide conservation plan accordingly. Finally, we stress the importance of taking into account the sources of confusion identified, as well as the complexity of biodiversity changes, in order to implement effective conservation strategies. In particular, biodiversity dynamics are almost systematically assumed to be linear, while non‐linear trends are largely neglected. Clarifying the sources of confusion in global biodiversity trends should strengthen large‐scale biodiversity monitoring and conservation. [ABSTRACT FROM AUTHOR]

Aim: Seedling recruitment is a vital process for forest regeneration and is influenced by various factors such as stand composition, climate, and soil disturbance. We conducted a long‐term field experiment (18 years) to study the effects of these factors and their interactions on seedling recruitment. Location: Our study focused on five main species in boreal mixed woods of eastern Canada: trembling aspen (Populus tremuloides), paper birch (Betula papyrifera), white spruce (Picea glauca), balsam fir (Abies balsamea), and white cedar (Thuja occidentalis). Methods: Sixteen 1‐m2 seedling monitoring subplots were set up in each of seven stands originating from different wildfires (fire years ranging from 1760 to 1944), with a soil scarification treatment applied to every other subplot. Annual new seedling counts were related to growing‐season climate (mean temperature, growing degree days and drought code), scarification, and stand effects via a Bayesian generalized linear mixed model. Results: Soil scarification had a large positive effect on seedling recruitment for three species (aspen, birch and spruce). As expected, high mean temperatures during the seed production period (two years prior to seedling emergence) increased seedling recruitment for all species but aspen. Contrary to other studies, we did not find a positive effect of dry conditions during the seed production period. Furthermore, high values of growing degree days suppressed conifer seedling recruitment. Except for white cedar, basal area was weakly correlated with seedling abundance, suggesting a small number of reproductive individuals is sufficient to saturate seedling recruitment. Conclusion: Our findings underscore the importance of considering multiple factors, such as soil disturbance, climate, and stand composition, as well as their effects on different life stages when developing effective forest management strategies to promote regeneration in boreal mixed‐wood ecosystems. [ABSTRACT FROM AUTHOR]

Climate change is increasing the frequency of extreme weather events, with low‐income countries being disproportionately impacted. However, these countries often face market frictions that hinder their ability to adopt effective adaptation strategies. In this paper, I explore the role of credit market failures in limiting adaptation. To achieve this, I collaborate with a large microfinance institution and offer a randomly selected group of farmers access to guaranteed credit through an "Emergency Loan" following a negative climate shock. I document three key results. First, farmers who have access to the emergency loan make less costly adaptation choices and are less severely affected when a flood occurs. Second, I find no evidence of adverse spillover effects on households that did not receive the Emergency Loan. Finally, I demonstrate that providing the Emergency Loan is profitable for the microfinance institution, making it a viable tool for the private sector to employ in similar circumstances. [ABSTRACT FROM AUTHOR]

Aim: The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location: Conterminous United States. Time Period: 2010–2021. Major Taxa Studied: Twenty‐five species of mammals. Methods: We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results: Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard‐mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine‐scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions: Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa. [ABSTRACT FROM AUTHOR]

A machine learning method is used to identify sources of long‐term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near‐surface zonal wind (U10)). Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has apredictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long‐lead signal originates from coupled wind‐SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO. Plain Language Summary: Many extreme events, such as floods or droughts, can be attributed to the El Niño Southern Oscillation, a mode of large‐scale ocean‐atmosphere coupled variability in the tropical Pacific Ocean occurring with a period of approximately 4 years. In this analysis, we use a machine learning methodology to disentangle the key atmospheric and oceanic ENSO components' relative contribution to its predictability, particularly the role of near‐surface 10‐m zonal wind. We quantify the potential for improved ENSO predictions for up to 2 years in advance and present a mechanistic understanding of the location of the sources of predictability. While equatorial sea surface temperature represents the primary source of ENSO predictability, the equatorial U10 plays a vital role from late spring to fall, from 1 to 2 years in advance. The enhanced predictability skill is shown to be linked to an SST anomaly originating in the Indian Ocean. The ML model used provides a new way to get new insights into the sources of predictability for ENSO and can be used as a simple but powerful tool to improve the underlying mechanistic understanding. Key Points: A deep learning‐based approach suggests near‐surface 10 m wind to play a significant role in providing skills for long‐term ENSO predictabilityThe skill of the 10‐m zonal wind is large from 12 months up to 23 months in advance (depending on the season)The signal is generated by coupled wind‐SST interactions in the Indian Ocean and later propagates across the Pacific [ABSTRACT FROM AUTHOR]

Britain's power system has shifted towards a major contribution from wind energy. However, wind is highly variable, and exceptionally low wind events can simultaneously occur with cold conditions, which increase demand. These conditions can pose a threat for the security of energy supply. Here we use bias‐corrected wind supply data and the estimated temperature‐related part of demand to analyse events of potential weather‐related energy shortfall based on the historic meteorological record. We conduct sensitivity studies with varying scenarios of Britain's total wind energy capacity and the temperature sensitivity of national demand. These scenarios are estimates for present‐day conditions as well as potential future changes of the power system. We apply a new methodology to estimate the potential severity of an event for the power system, and analyse the atmospheric conditions associated with the most severe events. We find that events of potentially severe shortfall are relatively rare and short‐lived, and often occur with an atmospheric pattern broadly resembling a negative North Atlantic Oscillation. This broad tendency emerges from a wide range of individual daily weather patterns that cause cold and still conditions. With an increase in wind capacity, it is likely that severe events will become rarer, although the most severe days of the record are relatively insensitive to changes in wind supply and temperature sensitivity of demand under our assumptions. [ABSTRACT FROM AUTHOR]

Hemp (Cannabis sativa L.) has historically played a vital role in agriculture across the globe. Feral and wild populations have served as genetic resources for breeding, conservation, and adaptation to changing environmental conditions. However, feral populations of Cannabis, specifically in the Midwestern United States, remain poorly understood. This study aims to characterize the abiotic tolerances of these populations, estimate suitable areas, identify regions at risk of abiotic suitability change, and highlight the utility of ecological niche models (ENMs) in germplasm conservation. The Maxent algorithm was used to construct a series of ENMs. Validation metrics and MOP (Mobility‐oriented Parity) analysis were used to assess extrapolation risk and model performance. We also projected the final projected under current and future climate scenarios (2021–2040 and 2061–2080) to assess how abiotic suitability changes with time. Climate change scenarios indicated an expansion of suitable habitat, with priority areas for germplasm collection in Indiana, Illinois, Kansas, Missouri, and Nebraska. This study demonstrates the application of ENMs for characterizing feral Cannabis populations and highlights their value in germplasm conservation and breeding efforts. Populations of feral C. sativa in the Midwest are of high interest, and future research should focus on utilizing tools to aid the collection of materials for the characterization of genetic diversity and adaptation to a changing climate. [ABSTRACT FROM AUTHOR]

Despite increasing exposure to flooding and associated financial damages, estimates suggest more than two‐thirds of flood‐exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the financial solvency of the United States National Flood Insurance Program. We study whether repeated exposure to flood events, especially the disaster‐scale floods that are expected to become more frequent in a warming climate, could spur insurance adoption. Using improved estimates of residential insurance take‐up in locations where such insurance is voluntary, and exploiting variation in the frequency and severity of flood events over time, we quantify how flood events impact local insurance demand. We find that a flood disaster declaration in a given year increases the take‐up rate of insurance by 7% in the following year, but that the effect diminishes in subsequent years and is gone after 5 years. This effect is more short‐lived in counties in inland states that do not border the Gulf and Atlantic coasts. We also find that the effect of a flood on take‐up is substantially larger if there was also a flood in the previous year, and that recent disasters are more salient for homeowners whose primary residences are exposed to a disaster declaration compared to non‐primary residences. Overall, these findings suggest that relying on households to self‐adapt to increasing flood risks in a changing climate is insufficient for closing the insurance protection gap. Plain Language Summary: Disaster‐scale floods are expected to become more frequent in a warming climate, impacting areas where insurance is not mandated. Many flood‐exposed properties are currently uninsured, potentially undermining the climate resilience of communities. We investigate whether households might adapt to the changing flood risk by purchasing and maintaining insurance over time. We find that while disaster‐scale floods increase insurance demand, this effect is temporary, even among counties experiencing consecutive disaster‐scale flood years. This effect is more salient among inland counties as well as among homeowners whose primary residence is exposed to a disaster‐scale flood. We conclude that relying on households to self‐adapt by maintaining insurance is insufficient for closing the insurance protection gap. Key Points: Increasing flood risk is impacting areas where flood insurance is not currently mandatedConsecutive disaster‐scale flood years increase insurance take‐up, but this effect diminishes over timeRelying on the autonomous adaptation of households will be insufficient for closing the insurance protection gap [ABSTRACT FROM AUTHOR]

Aerosol increases over the 20th century delayed the rate at which Earth warmed as a result of increases in greenhouse gases (GHGs). Aggressive aerosol mitigation policies arrested aerosol radiative forcing from ∼1980 to ∼2010. Recent evidence supports decreases in forcing magnitude since then. Using the approximate partial radiative perturbation (APRP) method, future shortwave aerosol effective radiative forcing changes are isolated from other shortwave changes in an 18‐member ensemble of ScenarioMIP projections from phase 6 of the Coupled Model Intercomparison Project (CMIP6). APRP‐derived near‐term (2020–2050) aerosol forcing trends are correlated with published model emulation values but are 30%–50% weaker. Differences are likely explained by location shifts of aerosol‐impacting emissions and their resultant influences on susceptible clouds. Despite weaker changes, implementation of aggressive aerosol cleanup policies will have a major impact on global warming rates over 2020–2050. APRP‐derived aerosol radiative forcings are used together with a forcing and impulse response model to estimate global temperature trends. Strong mitigation of GHGs, as in SSP1‐2.6, likely prevents warming exceeding 2C since preindustrial but the strong aerosol cleanup in this scenario increases the probability of exceeding 2C by 2050 from near zero without aerosol changes to 6% with cleanup. When the same aerosol forcing is applied to a more likely GHG forcing scenario (i.e., SSP2‐4.5), aggressive aerosol cleanup more than doubles the probability of reaching 2C by 2050 from 30% to 80%. It is thus critical to quantify and simulate the impacts of changes in aerosol radiative forcing over the next few decades. Plain Language Summary: Over the 20th century, fossil fuel burning led to increased concentrations of greenhouse gases (GHGs) and small particles known as aerosols. Aerosols scatter sunlight back to space and enhance cloud brightness and longevity, thus cooling Earth. The amount of warming since the pre‐industrial depends upon both GHGs and aerosol. After ∼1980, air quality improvements led to a reduction in this cooling effect, unmasking some of the GHG potential to warm the planet. This has reinforced the importance of understanding the interplay between aerosols and GHGs in climate projections. To examine this sensitivity, we use a set of global climate model simulations forced by a variety of future GHG and aerosol concentration based on plausible socioeconomic pathways. Shifts in the location of regional aerosol emissions has an impact on the global climate, influencing the accuracy of our predictions for Earth's future warming as measured by the probability of increasing global temperatures by 2C by 2050 compared to pre‐industrial. Under plausible GHG scenarios, aggressive aerosol cleanup policies can more than double the probability of crossing this threshold. This emphasizes the urgency of improving our simulations in order to accurately predict and quantify the impact of aerosols over the next few decades. Key Points: Aerosol forcing changes are isolated from CMIP6 projected scenarios using an approximate partial radiative perturbation methodAerosol‐cloud interactions dominate future aerosol forcing changes from cleanup policies over the period 2020–2050Likelihood of maintaining global warming to below 2C will be strongly reduced by implementing aggressive near‐term aerosol cleanup policies [ABSTRACT FROM AUTHOR]

Today, volcanic sulfur emissions into the atmosphere are measured spectroscopically from the ground, air and space. For eruptions prior to the satellite era, two main sulfur proxies are used, the rock and ice core records, as illustrated by Peccia et al. (2023, https://doi.org/10.1029/2023gl103334). The first approach is based on calculations of the sulfur content of the magma, while the second uses traces of sulfur deposited in ice. Both approaches have their limitations. For glaciochemistry, the volcano responsible for a sulfur anomaly is often unknown and the atmospheric pathway by which the sulfur reached the ice uncertain. The petrologic method relies, too, on uncertain estimates of eruption size and a number of geochemical assumptions that are hard to verify. A deeper knowledge of processes occurring both within magma bodies prior to eruption, and within volcanic plumes in the atmosphere is needed to further our understanding of the impacts of volcanism on climate. Plain Language Summary: Volcanic emissions of sulfur gases during large eruptions can change the global climate. Today, advanced spectroscopic tools allow us to measure sulfur from the ground, air and space. But to understand the impacts of volcanic activity on climate back through history we need to estimate volcanic sulfur releases in other ways. Clues can be found in the pumice deposits found around a volcano and, perhaps more surprisingly, in the ice caps of the polar regions, which receive the eventual fallout from large eruptions. Both approaches have limitations that are addressed in a recent work by Peccia et al. (2023, https://doi.org/10.1029/2023gl103334) and that are at the focus of our commentary. Key Points: The impact of volcanic activity on climate remains difficult to quantify for eruptions prior to the instrumental eraThe petrological and glaciochemical methods each provide answers, but are still fraught with large uncertaintiesWe need to further our understanding of the physicochemical processes occurring both within magma reservoirs and volcanic plumes [ABSTRACT FROM AUTHOR]

Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector‐borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large‐scale analyses from geopolitical frameworks to more ecologically meaningful regions. Plain Language Summary: In the United States (US), geopolitical subdivisions like states and counties are the governing regions that collect public health data and implement health interventions like risk messaging, mask mandates, or spraying chemicals for mosquito control. These subdivisions were mostly decided by politics and other human activity, not similarities in ecosystems and climate. This can make it difficult to understand how climate affects illnesses like mosquito‐borne disease since mosquitoes will live within specific ecosystems and aren't confined to geopolitical boundaries as health data are. We explored this idea by analyzing the relationships between temperature, precipitation, and the mosquito‐borne pathogen called West Nile virus (WNV) from 2005 to 2019. We grouped our data using two different regional boundaries: (a) climate regions from the National Oceanic and Atmospheric Administration (NOAA), primarily based on state boundaries, and (b) ecoregions from the Environmental Protection Agency (EPA), based on similar ecosystems. We found that these groupings resulted in some contradictory results. The results using EPA ecoregions more often agreed with biological hypotheses, so it may be a better approach to dividing the US for similar studies. We encourage other scientists to consider this conceptual shift from analyzing data by geopolitical boundaries to more ecologically meaningful regions. Key Points: While health data is collected within geopolitical boundaries, environmental disease dynamics are influenced by ecosystem characteristicsFor West Nile virus, correlations between cases and climate were different depending on geopolitical or ecosystem regional groupings of dataWe propose a conceptual shift from analyzing climate and health data at geopolitical boundaries to more ecologically meaningful regions [ABSTRACT FROM AUTHOR]

Observations and models show that near‐surface relative humidity is nearly constant at ∼80% over the ocean in the current climate, and almost invariant in the global mean in projected future climates. Here, this behavior is investigated through the development of a simple theoretical model for near‐surface relative humidity by considering the moisture balance above a uniform ocean surface. The relative humidity is predicted to depend on only the near‐surface wind speed, air‐surface temperature difference, surface wetness and large‐scale moisture convergence. Although developed in the context of moist over‐ocean convection, the theory is able to determine the relative humidity in a suite of idealized simulations over both wet and dry surfaces with a root‐mean‐square error of less than 3%. The theory also predicts the climatology of relative humidity over the ocean with a root‐mean‐square error of less than 3%. The theory thus provides a theoretical basis for investigating changes in relative humidity over the ocean, water vapor feedbacks and the water cycle in current and future climates. Plain Language Summary: Relative humidity is the water content of air relative to its saturation value. Observations and climate models show that global mean near‐surface relative humidity is almost invariant in a changing climate, an observation which underpins the enhancement of the water cycle with increasing global mean temperatures and surface radiative feedbacks. Furthermore, relative humidity over the ocean, whether in the tropics or polar regions, takes a near uniform value of ∼80%. However, a theoretical basis for this remarkable invariance of relative humidity has not been previously established. Here we show that near‐surface relative humidity is controlled by the geometry of moist convection over the ocean surface. Its value is set by a non‐dimensional geometric constant and is independent of the absolute air temperature, with weak dependence on near‐surface wind speeds, air‐surface temperature difference and large‐scale moisture convergence. Hence, the over ocean relative humidity is largely climate and latitude invariant. Key Points: Convection above the ocean surface exhibits a characteristic geometry that controls the value of near‐surface relative humidityA quadratic equation is derived to predict near‐surface relative humidity in terms of known surface properties and a geometric constantThe equation predicts the relative humidity in idealized simulations and ocean observations with less than 3% error [ABSTRACT FROM AUTHOR]

As both the population and economic output of India continue to grow, so does its demand for electricity. Coupled with an increasing determination to transition to net zero, India has responded to this rising demand by rapidly expanding its installed renewable capacity: an increase of 60% in the last 5 years has been driven largely by a quintupling of installed solar capacity. In this study, we use broad variety of data sources to quantify potential and realized capacity over India from 1979 to 2022. For potential capacity, we identify spatiotemporal patterns in solar, wind, hydro and wave power. We show that solar capacity factor is relatively homogeneous across India, except over the western Himalaya, and is highest during the pre‐monsoon. Wind capacity factor is highest during the summer monsoon, and has high values off the southern coast, along the Western Ghats, and in Gujarat. We argue that wave power could be a useful source of renewable energy for the Andaman and Nicobar Islands, which are not connected to the main Indian power grid. Using gridded estimates of existing installed capacity combined with our historical capacity factor dataset, we create a simple but effective renewable production model. We use this model to identify weaknesses in the existing grid—particularly a lack of complementarity between wind and solar production in north India, and vulnerability to high‐deficit generation in the winter. We discuss potential avenues for future renewable investment to counter existing seasonality problems, principally offshore wind and high‐altitude solar. [ABSTRACT FROM AUTHOR]

Leaf senescence have shed light on its contribution to the recovery of nutrients and signaling of the transition from the active to the dormant stage in winter. The manner by which climatic and edaphic factors and plant phylogeny affect the senescent leaf economics spectrum (more mineral elements included) of different growth habits along elevational gradients in mountain ecosystems is highly uncertain. To fill this knowledge gap, we examined 15 representative subalpine forest species' senescent leaf functional traits (i.e., specific leaf area [SLA], leaf dry matter content [DMC], pH, carbon [C], nitrogen [N], phosphorus [P], potassium [K], calcium [Ca], sodium [Na], magnesium [Mg], manganese [Mn], aluminum [Al], iron [Fe], C:N, N:P, and C:P ratios) to determine the senescent leaf economics spectrum along elevational gradients in the Hengduan Mountains, China. Our results demonstrate that all the functional traits of senescent leaf showed strong correlations with one another, and K was observed as the hub for the leaf economics spectrum. Irrespective of the difference among growth habits (arbor vs. shrub), leaf habits (deciduous vs. evergreen), and leaf types (broad vs. needle), the variations in the two‐dimensional spectrum of the senescent leaf trait exhibited uniformly positive associations with elevation, primarily attributed to climatic drivers. However, the modulation of the senescent leaf economics spectrum by soil properties and plant phylogeny was surprisingly modest. Collectively, our results revealed that the resource trade‐off of senescent leaf showed a trend from acquisition to conservation along elevational gradients, which could broaden the plant economics spectrum to include senescent leaf. [ABSTRACT FROM AUTHOR]

Global Climate Model tuning (calibration) is a tedious and time‐consuming process, with high‐dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand‐picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere‐only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient‐based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly‐tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high‐dimensional output and operating in a fraction of the time. Plain Language Summary: The systems governing the atmosphere and other physical processes on Earth are complex, and climate scientists use Earth Systems Models (ESMs) based on physical and computational representations of these processes to create simulations that mimic the behavior of Earth's climate. Since ESMs are defined at a lower resolution than some processes require, parameters are introduced to address the uncertainty in the approximation. The traditional approach is to hand‐tune these parameters using expert knowledge, a time‐consuming process that involves matching climate model output to observational data on Earth, as closely as possible. Instead, we introduce an alternative "automated calibration" framework that chooses such parameters automatically and in a fraction of the time. This is done by building a very fast machine‐learning‐based substitute (surrogate) for the ESM to predict variability in the climatology for 11 different spatial fields over four seasons, and then using this surrogate to search for the parameters that best match climate observations. We apply our framework to the atmospheric component of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2, and demonstrate an improvement of 2.7% in root‐mean‐squared‐error on average across the different variables and seasons, compared to the hand‐tuned solution. Key Points: We apply an autocalibration method to uncertain parameters in the Energy Exascale Earth System Model version 2 (E3SMv2) atmosphere modelWe use a 250‐member perturbed parameter E3SMv2 ensemble to train a polynomial chaos surrogate model and optimize input parametersThe autocalibrated parameter set improves E3SMv2's root‐mean‐squared‐error against observations by 2.7% [ABSTRACT FROM AUTHOR]

Desert locust (Schistocerca gregaria) infestations cause significant damage to crops and pastureland, impeding food security and livelihoods globally. In recent years, some East African countries have suffered severe desert locust outbreaks, causing significant harm to agriculture and local communities. To comprehensively understand and mitigate the socio‐economic impacts of this pest, an integrated assessment approach is crucial. This study proposes an integrated assessment model that combines ecological, economic, and social dimensions to analyze the multifaceted impacts of the desert locust on the rate of urbanization changes, farming expansion, and food production and how they lead to food unavailability (demand, supply, and price) and food and nutrition insecurity in Eastern Africa. A system dynamics‐based assessment model was first developed for Sudan using the dynamic relations among the factors and their variations over time and how they affect the socio‐economic variables. The developed model was used to spatially simulate the impacts over the entire Eastern Africa. Various scenarios were further simulated and analyzed, incorporating different policy options to effectively mitigate the impacts. Results demonstrated that in all the countries, desert locusts extensively contribute to the slowing down of farming expansion and food production and supply. Consequently, these factors lead to increased urbanization rates through people rural‐urban migration and food demand at different magnitudes across different countries. By considering both short‐term and long‐term effects, this approach aims to provide policymakers, researchers, and practitioners with a holistic understanding of the complex dynamics involved and inform effective management strategies. For example, communities equipped with accurate breeding detection tools and employing an integrated pest management strategy combining chemical pesticides and biopesticides had the highest potential for effectively mitigating the future impacts of desert locusts for enhancing community livelihood. Plain Language Summary: Desert locusts are a major problem for agriculture and communities in East Africa. They eat crops and grasslands, which can lead to food shortages and higher prices. To better understand and deal with this issue, we have created a model that looks at how locusts affect things like farming, urbanization, and food supply. We studied this in Sudan and then applied it to the whole of East Africa. The results showed that locusts make farming harder and push more people to move from rural areas to cities. This causes more demand for food in cities. The study tested different ways to deal with locusts and found that communities with good tools for detecting locusts and using both chemical and biopesticide control methods were the best at reducing the impact of locusts on their lives. This study gives policymakers and others a better understanding of how locusts affect communities and how to manage them effectively in the future. Key Points: In Eastern Africa, desert locusts swarm in vast numbers, devouring crops, and vegetation, causing crop production losses ranging from 42% to 69%The system dynamics model was employed for a comprehensive analysis of the impacts, scenarios, and policy options for mitigating desert locust effectsDesert locusts hinder farming, reduce food production, spur urbanization, and heighten food insecurity across affected Eastern African countries [ABSTRACT FROM AUTHOR]

The Italian territory is located at the heart of one of the global hot spots of climate change, where the implementation of climate‐smart land management practices is imperative to guarantee the present and future maintenance of ecosystem functions as well as the sustainability of human socioeconomic activities. The project HIGHLANDER (HIGH performance computing to support smart LAND sERvices) led by Cineca aims at building a comprehensive and multi‐sector framework for land‐management decision‐making in Italy. The project relies on high quality information on different components of the landscape, with a focus on climate‐driven processes, and state‐of‐the‐art computing infrastructures. The HIGHLANDER Data Portal maximizes the impact of HIGHLANDER results by providing access to data products and services. In this article, we describe the architectural features of the platform, as well as the available HIGHLANDER datasets and downstream applications. [ABSTRACT FROM AUTHOR]

Aim: As climate change accelerates, assessing how climate shapes gene flow and neutral and adaptive genetic differentiation on landscapes is increasingly important. Aardvarks (Orycteropus afer) are ecologically important in sub‐Saharan Africa but are sensitive to human pressures and increasing aridity. We used individual, population and landscape genetic approaches to infer the influence of landscape, climate and potential adaptive differences on gene flow. Location: We surveyed 8 protected and 4 privately owned areas in South Africa, 2 protected areas in Eswatini and one location in Kenya during 2016–2018. Methods: We developed microsatellite markers and methods for DNA extraction from faeces, collected and genotyped faecal samples from focal areas and estimated genetic structure. We inferred space use from individual redetections, tested for close relatives and estimated genetic neighbourhood distance. We applied individual‐based landscape genetic analyses at multiple scales to test hypotheses about genetic differentiation by landscape resistance and potential adaptive differences. Results: We developed 19 variable microsatellite loci and collected 253 faecal samples from 13 focal areas. We genotyped 104 samples successfully at ≥8 loci as needed for individual identification. The genetic structure suggested 3 regional divisions in South Africa. We detected individuals at locations ≤7.3 km distant and closely related individuals at ≤44 km; genetic neighbourhood distance was <55 km. Lower precipitation increased landscape resistance and strongly predicted genetic differentiation at most spatial scales. Temperature differences at sampling sites also influenced structure, suggesting climate‐associated adaptive differences. Main Conclusions: The genetic structure of aardvarks is strongly shaped by climate, with arid areas limiting gene flow and reflects apparent isolation by adaptation associated with temperature. Dispersal distances likely are <55 km. The markers we developed will facilitate studies of space use, dispersal, population density or survival. Aridification will increase fragmentation and we recommend monitoring aardvark presence as an indicator of ecosystem change associated with aridification. [ABSTRACT FROM AUTHOR]

Stratospheric Aerosol Injection (SAI) is a proposed method of climate intervention aiming to reduce the impacts of human‐induced global warming by reflecting a portion of incoming solar radiation. Many studies have demonstrated that SAI would successfully reduce global‐mean surface air temperatures; however the vast array of model scenarios and strategies result in a diverse range of climate impacts. Here we compare two SAI strategies—a quasi‐ equatorial injection and a multi‐latitude off‐equatorial injection—simulated with the UK Earth System Model (UKESM1), both aiming to reduce the global‐mean surface temperature from that of a high‐end emissions scenario to that of a moderate emissions scenario. We compare changes in the surface and stratospheric climate under each strategy to determine how the climate response depends on the injection location. In agreement with previous studies, an equatorial injection results in a tropospheric overcooling in the tropics and a residual warming in the polar regions, with substantial changes to stratospheric temperatures, water vapor and circulation. Previous comparisons of equatorial versus off‐equatorial injection strategies are limited to two studies using different versions of the Community Earth System Model. Our study evaluates how the climate responds in UKESM1 under these injection strategies. Our results are broadly consistent with previous findings, concluding that an off‐equatorial injection strategy can minimize regional surface temperature and precipitation changes relative to the target. We also present more in‐depth analysis of the associated changes in Hadley Circulation and regional temperature changes, and call for a new series of inter‐model SAI comparisons using an off‐equatorial strategy. Plain Language Summary: Stratospheric Aerosol Injection (SAI) is a method to tackle the impacts of global warming and involves reflecting some of the sun's rays away from Earth. Different strategies for implementing SAI can have various effects on the climate. This study compares two strategies—one injecting at the equator and the other at different latitudes. Both strategies successfully lower global temperatures, but they also lead to different regional climate changes. The equatorial strategy cools the tropics too much and doesn't cool the poles enough. Whereas the off‐equatorial strategy minimizes some of the negative impacts seen in the equatorial strategy. In summary, injecting aerosols away from the equator avoids unfavorable climate impacts. Key Points: We compare the climate impacts of equatorial and multi‐latitude Stratospheric Aerosol Injection (SAI) strategies under the Geoengineering Model Intercomparison Project G6 frameworkWe demonstrate that an off‐equatorial multi‐latitude injection strategy minimizes unfavorable climate impactsThis research highlights the importance of injection location in determining the impacts of SAI on the climate [ABSTRACT FROM AUTHOR]

While some previous studies examined the contribution of Eastern Pacific (EP) hurricanes toward precipitation in the arid Southwest US (SWUS), their potential to influence wildfires in that region has not been explored. Here we show, using observations and simulations from the Energy Exascale Earth System Model (E3SM), that recurving EP hurricanes modulate the wildfire environment in the SWUS by increasing precipitation and soil moisture, and reducing the vapor pressure deficit. This is especially the case during late season months of September–October when the likelihood of storms to recurve and make landfall increases. Further, analysis of burnt area observations reveals that for the months of September–October, recurving EP hurricanes may significantly reduce the prevalence of wildfires in the SWUS. Finally, E3SM simulations indicate that late season EP hurricanes have been on the decline, with important implications for wildfires in the SWUS. Plain Language Summary: A majority of Eastern Pacific (EP) hurricanes form and traverse westwards away from the North American coastline. Therefore, compared to tropical cyclones in some other basins, EP hurricanes have historically received less attention. However, some previous studies have shown that recurving EP hurricanes can contribute significantly to precipitation in the arid Southwest US (SWUS) region. Here, we build on these studies and demonstrate that recurving EP hurricanes significantly modulate the SWUS wildfire environment. During years with recurving EP hurricanes, the precipitation and soil moisture are enhanced, and the vapor pressure deficit is reduced, over the SWUS region. Therefore, during late season months of September–October, when recurving EP hurricanes occur, they make the environment less favorable for wildfires in the SWUS region. With climate models showing a long‐term decline in recurving EP hurricanes and the precipitation associated with them over the SWUS region, our study has substantial implications for autumn wildfires of that region. Key Points: Eastern Pacific (EP) hurricanes that recurve tend to enhance precipitation and reduce vapor pressure deficit in the Southwest US regionDuring September–October, recurving EP hurricanes may make the environment less favorable for wildfires in the Southwest USClimate model simulations suggest that Southwest US precipitation associated with recurving EP hurricanes has decreased [ABSTRACT FROM AUTHOR]

Global heat stress is a phenomenon that impacts the livelihood of humans worldwide. Due to climate change, heatwaves are already increasing negatively impact outdoor laborers and activities. However, calculating heat stress on a global scale is disparaged due to the interplay and treatment of temperature, humidity, and radiation. To help resolve this issue, the Wet Bulb Globe Temperature (WBGT), a standardized heat stress metric, is implemented into the Community Land Model (CLM5), the land surface component of the Community Earth System Model (CESM2). This resolves a long lasting, complex issue within global heat stress: the treatment of solar and thermal radiation. A default configuration of CLM5 is executed and shows the advantages of simulating the WBGT within multiple environments. Additionally, two commonly used WBGT approximations are implemented for solar exposed (sWBGT) and shaded (FiWBGT) conditions. The 1995 Chicago Heatwave is examined as a case study, focusing on the rural regions impacted by the heatwave. Derivative functions of labor capacity show that assumptions about calculating a non‐linear algorithm generate non‐negligible biases that can grossly over or underestimate the impact of heat stress on future climate change projections. For example, a difference of 0.5°C from WBGT can result in >10% change in labor capacity. Using a conservative difference of ±0.3°C, 100% of land surface extreme sWBGT values and >77% extreme shaded conditions (FiWBGT) differ from WBGT. Therefore, to accurately assess the direct exposure, risk, and damage from climate change on people, it is critical to implement diagnostics directly into Earth system models. Plain Language Summary: Heat stress is a global phenomenon that impacts people of all backgrounds. However, calculating heat stress on a global scale is difficult due to the interplay between temperature, humidity, and radiation. Unfortunately, because there are no global observation data sets that include radiation to accurately calculate heat stress, this leaves climate models and theory to determine heat stress. To that end, metrics that include radiation, like the Wet Bulb Globe Temperature, are sensitive to small changes in environmental weather, such as cloud variability. Furthermore, impacts derived from heat stress are likewise sensitive to small changes in metrics. Implementing representations of radiation‐based heat stress metrics within climate models produces accurate heat stress calculations, as compared to results produced from using offline climate output, improving weather forecasts for today's climate and projections into future damages from climate change. Key Points: Heat stress derived from offline global output introduces large biases in their results, which in turn produce errors in impact assessmentsRadiation‐based heat stress metrics implemented into Earth system model frameworks is recommended to reduce biases in results and outcomes [ABSTRACT FROM AUTHOR]

Camelina [Camelina sativa (L.) Crantz] is a Brassicaceae oilseed that is gaining interest worldwide as low‐maintenance crop for diverse biobased applications. One of the most important factors determining its productivity is climate. We conducted a bioclimate analysis in order to analyze the relationship between climatic factors and the productivity of spring‐type camelina seeded in the spring, and to identify regions of the world with potential for camelina in this scenario. Using the modelling tool CLIMEX, a bioclimatic model was developed for spring‐seeded spring‐type camelina to match distribution, reported seed yields and phenology records in North America. Distribution, yield, and phenology data from outside of North America were used as independent datasets for model validation and demonstrated that model projections agreed with published distribution records, reported spring‐seeded camelina yields, and closely predicted crop phenology in Europe, South America, and Asia. Sensitivity analysis, used to quantify the response of camelina to changes in precipitation and temperature, indicated that crop performance was more sensitive to moisture than temperature index parameters, suggesting that the yield potential of spring‐seeded camelina may be more strongly impacted by water‐limited conditions than by high temperatures. Incremental climate scenarios also revealed that spring‐seeded camelina production will exhibit yield shifts at the continental scale as temperature and precipitation deviate from current conditions. Yield data were compared with indices of climatic suitability to provide estimates of potential worldwide camelina productivity. This information was used to identify new areas where spring‐seeded camelina could be grown and areas that may permit expanded production, including eastern Europe, China, eastern Russia, Australia and New Zealand. Our model is the first to have taken a systematic approach to determine suitable regions for potential worldwide production of spring‐seeded camelina. [ABSTRACT FROM AUTHOR]

We investigate positive subtropical low cloud feedback mechanisms in climate models which have performed the CMIP6/CFMIP‐3 AMIP and AMIP uniform +4K experiments while saving CFMIP‐3 process diagnostics on model levels. Our analysis focuses on the trade cumulus/stratocumulus transition region between California and Hawaii, where positive low cloud feedbacks are present in the JJA season. We introduce a methodology to test various positive cloud feedback mechanisms proposed in the literature as the main causes of the low cloud responses in the models. Causal hypotheses are tested by comparing their predictions with the models' responses of clouds, cloud controlling factors, boundary layer depth and temperature/humidity tendencies to climate warming. Changes in boundary layer depth, relative humidity in the cloud layer, convective moistening rate and large‐scale humidity advection at the top of the boundary layer are shown to be crucial for identifying the main causes of the low cloud reductions in the models. For the cases examined, our approach narrows down the seven mechanisms considered to between one and three remaining candidates for each model. No single mechanism considered can explain the feedback in all of the models at the locations examined, but the surface latent heat flux/convective entrainment mechanism remains a candidate for BCC‐CSM2‐MR, IPSL‐CM6A‐LR, and MRI‐ESM2.0, while the surface upwelling longwave mechanism remains for CESM2, HadGEM3‐GC3.1‐LL, and MIROC6. Plain Language Summary: Climate models show reductions in low‐level clouds with the warming climate which are poorly understood. We examine cloud changes between California and Hawaii in six climate models. We consider seven potential explanations for the changes. We find that examining changes in the height of low level clouds, the moistening of the atmosphere by rising plumes of moist air, the humidity of the air and the rate at which dry air is mixed into the clouds from above allows us to narrow down the number of explanations compatible with each model. Key Points: Hypotheses for positive cumulus/stratocumulus feedback mechanisms are tested in six climate modelsWe narrow down the seven mechanisms considered to between one and three candidates for each modelBoundary layer depth, relative humidity, convective moistening, and humidity advection are key [ABSTRACT FROM AUTHOR]