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This paper makes an argument for a geography of girlhood, located at the intersection of historical geographies of globalisation and empire on the one hand, and feminist interventions in the geography of childhood and youth on the other. A focus on girlhood, I argue, opens up a debate on the discipline's own implication in a debate on climate science, moral hierarchies of civilisation and reproductive health at which intersection the category of 'girl' was materialised in the 19th century. This focus extends and historicises the argument made by scholars like Nicola Ansel that geographies of childhood speak not only to intimate scales of experience ‐ such as the home and neighbourhood—but instead suggest the ways in which everyday life is implicated in the scale of the global and the geopolitical. Drawing on an inter‐disciplinary scholarship, the paper argues that debates on gender and maturity—converging on the figure of the 'girl'—shaped raced and classed imaginaries of progress in the 19th and 20th centuries. Through this, the paper demonstrates that 'girlhood' is at the heart of historical geographies of urban planning, social care, and health, as well as indexing the continuities in the transition from a colonial discourse of civilisation to a mid‐20th century concern with development. Finally, the paper asks how to write about girls through an archive that is almost obsessively fixated on them as subjects of education and reform, even whilst they rarely appear in it as speaking subjects. I argue that both an emergent focus on non‐textual objects as sources, as well as the use of ephemeral material—including notes, creative writing exercises from the classroom, school diaries etc.—alongside the official archive might open the scholarship up to a multi‐scalar analysis of girlhood as imbricated in larger global and national discursive and material practices. [ABSTRACT FROM AUTHOR]

The expression "lightning never strikes twice" is questioned in this paper because, among the randomness of lightning impacts, some spots are hit even more than twice year after year. This article introduces the recurrent lightning spots (RLS) concept, which are locations periodically impacted by cloud‐to‐ground lightning every consecutive year over a certain period. RLS are investigated in two regimes, with markedly different lightning climatology but similar orography, for 10 consecutive years: Catalonia (North East of Spain, Europe) and Barrancabermeja (North Central Colombia, South America). Results revealed 148 and 916 RLS in Catalonia and Barrancabermeja, respectively. RLS in both regions are typically found to be related to tall structures, mountain peaks, and steep terrain. The method allowed us to identify those tall towers and orographic relief frequently affected by lightning that are not detected with the mere computation of the ground flash density. In the case of Catalonia, some RLS are found offshore. Besides the scientific interest in understanding lightning, the new concept of RLS provides additional and valuable information applicable to lightning protection engineering. Plain Language Summary: The expression "lightning never strikes twice" is questioned in this paper because it shows that some spots are hit even more than twice: year after year. The RLSs (RLS) concept is presented, which are locations where lightning strike them periodically. The two regions of study with markedly different lightning climatology but similar orography are Catalonia (North East of Spain, Europe) and Barrancabermeja (in North Central Colombia, South America). RLS in both regions are typically found to be related to tall structures, mountain peaks and steep terrain. In the case of Catalonia, some RLS are found offshore. RLS new concept provides valuable information to lightning protection engineering. Key Points: Recurrent lightning spots (RLS) are targets with at least one cloud‐to‐ground lightning per year during a certain number of consecutive yearsIn Catalonia (Spain), 13% of the RLS are tall towers, 72% are mountainous peaks and 5% are offshore sitesThe method effectively finds tall structures affected by lightning in tropical climates [ABSTRACT FROM AUTHOR]

Large vertical shears in the E‐region neutral zonal winds can lead to ion convergences and contribute to plasma irregularities, but climatological studies of vertical shears of horizontal winds in a global scale are lacking due to the limitations of data coverage. The Ionospheric Connection Explorer (ICON) Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) has provided neutral wind observations with an unprecedented spatial coverage. In this study, the climatology of dayside E‐region neutral wind shears has been examined using 2‐years' data (2020–2021). Specifically, the study focuses on large wind shears with a magnitude larger than 20 m/s/km, since large wind shears are more likely to cause significant perturbation in the ionosphere‐thermosphere (I‐T) system. The results show that the probability of occurrence of large shears is strongly dependent on the altitude, with the vertical profile varying with shear direction, latitude, season, and local time. In general, below 110 km altitude, large negative shears of the eastward wind are most likely to happen during summer at 8–10 LT in 25°N–40°N latitudes, showing a high probability across nearly all longitudes. Meanwhile, large positive shears tend to occur in 10°S–10°N latitudes, with peak probabilities exhibiting roughly consistent longitudinal structures across 8–10 LT in all seasons. The discrepancies between positive and negative large shear distributions underlie different global tidal influences. The large‐shear occurrence probabilities above 110 km are generally small, except in latitudes above 25°N during the winter for positive shears. Plain Language Summary: Horizontal winds at ∼90–130 km above the Earth's surface often change rapidly with height. These large vertical gradients can cause structures and disturbances among charged particles. In this paper, we used eastward wind data from the Ionospheric Connection Explorer (ICON) to determine where and when large gradients are most likely to occur. We found that large gradients usually occur below 110 km. While both westward and eastward vertical gradients are influenced by location, season, and time of day, their patterns and strength of influence clearly differ. Key Points: Large vertical shears in the zonal wind (>20 m/s/km) are most likely to occur below 110 kmHighest probabilities of large shears occurrence is during summer at 8–10 LT for negative (westward) shears in the 25°N–40°N latitudesLocal‐time, longitudinal, and latitudinal patterns of large shears indicate the potential influence of tides such as DE3, DW1, and SW2 [ABSTRACT FROM AUTHOR]

Current soil‐ and land degradation seriously challenge our societies; it contributes to climate change, loss of biodiversity and loss of agricultural productions. Yet, soils are also seen as a major part of the solution, if maintained or restored to provide ecosystem services. Climate‐smart sustainable management of soils can provide options for soil health maintenance and restoration. In the European Union, the resource management and sustainability challenge are addressed in the Green Deal that, among other goals, aspires towards a healthy climate‐resilient agricultural sector that will produce sufficient products without damaging ecosystems and contribute to better biodiversity and mitigate climate change. The European Joint Programme (EJP) SOIL was set up to contribute to these goals by developing knowledge, tools and an integrated research community to foster climate‐smart sustainable agricultural soil management that provides a diversity of ecosystem service, such as adapting to and mitigating climate change, allowing sustainable food production, and sustaining soil biodiversity. This paper provides an overview of the potential of climate‐smart sustainable soil management research to the targets of the Green Deal that are related to soils most directly. The EJP SOIL EU‐wide consultation (interviews and questionnaires) and literature analysis (national and international reports and papers) done in the first year (2020–2021) generated a wealth of data. This data showed that there are specific manners to do research that are essential for it to be effective and efficient and that can actively contribute to the Green Deal targets. We concluded that research needs to be: (i) interdisciplinary, (ii) long‐term, (iii) multi‐scaled, from plot to landscape, (iv) evaluating trade‐offs of selected management options for ecosystem services and (v) co‐constructed with key stakeholders. Research on climate‐smart sustainable soil management should be developed (1) on plot scale when mobilizing soil processes and on landscape scale when addressing sediment and water connectivity and biodiversity management; and (2) address the enabling conditions through good governance, social acceptance and viable economic conditions. [ABSTRACT FROM AUTHOR]

We present an application of quantile generalized additive models (QGAMs) to study spatially compounding climate extremes, namely extremes that occur (near‐) simultaneously in geographically remote regions. We take as an example wintertime cold spells in North America and co‐occurring wet or windy extremes in Western Europe, which we collectively term Pan‐Atlantic compound extremes. QGAMS are largely novel in climate science applications and present a number of key advantages over conventional statistical models of weather extremes. Specifically, they remove the need for a direct identification and parametrization of the extremes themselves, since they model all quantiles of the distributions of interest. They thus make use of all information available, and not only of a small number of extreme values. Moreover, they do not require any a priori knowledge of the functional relationship between the predictors and the dependent variable. Here, we use QGAMs to both characterize the co‐occurrence statistics and investigate the role of possible dynamical drivers of the Pan‐Atlantic compound extremes. We find that cold spells in North America are a useful predictor of subsequent wet or windy extremes in Western Europe, and that QGAMs can predict those extremes more accurately than conventional peak‐over‐threshold models. Plain Language Summary: In this paper we propose a new data‐driven method to study climate extremes occurring simultaneously in multiple, possibly remote, locations. Such extremes can pose a greater threat to human societies than single, isolated extremes, as their effects may exacerbate each other and lead to correlated losses. The method we suggest requires fewer assumptions than conventional extreme value statistical techniques, and can help us to identify previously unknown relationships between the extremes themselves and their possible drivers. We exemplify its use by studying the co‐occurrence of periods of unusually cold weather in North America and subsequent uncommonly strong wind and abundant precipitation in Western Europe. We find that the new method has better predictive power for the European extremes than conventional statistical approaches. Furthermore, we confirm the results of previous studies suggesting an association between the wintertime extremes in North America and Western Europe. Key Points: Quantile general additive models (QGAMs) can model the relationship between compound climate extremes flexibly and robustlyNorth American cold spells show some predictive skill for wet or windy extremes in Western Europe, even when accounting for confoundersGiven relevant atmospheric predictors, QGAMs can predict these extremes more accurately than peak‐over‐threshold models in most regions [ABSTRACT FROM AUTHOR]

A three‐dimensional winter (DJF) climatology of Lagrangian diffusivity characterizing eddy mixing and transport in the northern middle atmosphere is presented. To emphasize aspects other than zonal averages, we use the theory of Lagrangian diffusivity (κyy) hitherto not applied in stratospheric contexts to our knowledge. Our formulation of Lagrangian diffusivity requires the calculation of parcel trajectories, which is made on isentropic surfaces. A Lagrangian descriptor is used to estimate the boundary of the stratospheric polar vortex (SPV). To characterize quasi‐geostrophic motions and their influence on the SPV we apply the wave activity flux (W) and Local Wave Activity (A) $(\mathcal{A})$. Our data set is the ERA5 reanalysis for the period 1979–2013. Results for κyy show important zonal asymmetries. In the lower and middle stratosphere, κyy is highest at midlatitudes, particularly around the prime meridian. This location is surrounded by manifolds associated with hyperbolic trajectories emanating from the outer SPV boundary. κyy is also high within the SPV, and near the locations where the SPV boundary is open. Zonal asymmetries are also clear in W at midlatitudes. The larger values of A $\mathcal{A}$ are at high latitudes and upstream of the opening of the vortex boundary. The role of quasi‐geostrophic waves on the south‐north shift of the midlatitude westerlies is highlighted. In particular, the waves contribute to open the SPV boundary at around 90W. The interannual variability of κyy is explored by contrasting winters with positive‐negative Northern Annular Mode index, and Sudden Stratospheric Warmings of displacement‐split type. Plain Language Summary: This paper applies a novel approach to studying eddy mixing and transport in the northern middle atmosphere during winter (December–January–February). The approach is based on the concept of Lagrangian diffusivity, a measure of how quickly air parcels mix together. Unlike traditional diagnostics that rely on longitudinal or contour‐based averages, Lagrangian diffusivity provides a detailed three‐dimensional view of the mixing. This approach, hitherto restricted to oceanographic applications, is a significant contribution of the present work. We employ a Lagrangian descriptor, a tool based on parcel trajectory length, to locate the stratospheric polar vortex SPV boundary. Also, we investigate the influence of quasi‐geostrophic motions on the SPV using wave activity flux and local wave activity (LWA). The data set is the ERA5 reanalysis from 1979 to 2013. The results reveal pronounced zonal asymmetries in Lagrangian diffusivity and wave activity flux. Mixing is highest at midlatitudes around the prime meridian and at locations within the SPV. LWA is elevated at high latitudes and upstream of the climatological vortex boundary opening, highlighting the role of quasi‐geostrophic waves in the southward displacement of midlatitude westerlies. Comparisons are made of diffusivity during weak and strong vortex winters, as well as of during winters with and without Sudden Stratospheric Warmings. Key Points: Locally, the largest values of Lagrangian diffusivity around the vortex are along the prime meridian at all levels in the stratosphereThe location of maximum diffusivity is bounded by the edge of the climatological displaced vortex and a manifold emanating from the vortex edgeWhen the Northern Annular Mode is positive, the vortex boundary closes and diffusivity is much lower inside [ABSTRACT FROM AUTHOR]

This paper details the creation of a tropical cyclone (TC) size dataset using the NCEP/NCAR Reanalysis I dataset for landfalling TCs along the United States coastline from 1948 to 2022. The radius of the outermost closed isobar (ROCI) is used as the size parameter. The dataset comprises landfall ROCI for 220 TCs. Storms are split into three zones (Texas–Alabama, Florida and Georgia–Maine) to determine if TC size varies geographically. Results showed a significant difference in landfall size, with Florida storms larger on average than the Texas–Alabama storms. Additionally, TC size increased with increasing intensity from tropical storm to Category 3, and storms tended to be larger later in the hurricane season, but there was no significant trend in landfall size over the 75‐year period. ROCI exhibited statistically significant positive correlations with longitude and wind speed and a negative correlation with the outermost closed isobar's pressure. The dataset's creation is an example of how reanalysis datasets can be used to develop a TC size climatology. [ABSTRACT FROM AUTHOR]

This document provides a summary of a hackathon event organized by the Ministry of Justice (MoJ) and Aon, a reinsurance company, to explore the social impacts of climate change. The hackathon focused on developing consistent scenarios for future climate risk, understanding uncertainties in European windstorm modeling, and providing real-time context for extreme weather events. The teams developed tools and methodologies to assess the severity and potential impact of extreme events on insured losses. The collaboration between the MoJ, Aon, and participants from various institutions aims to further research and develop papers and grant proposals in these areas. [Extracted from the article]

Separating existing historical precipitation data into solid and liquid precipitation remains a challenge in the study of climate change, extreme precipitation, and hydrological modelling. Based on historical daily air temperature and precipitation data, as well as visual observations of precipitation phase (weather phenomena records) in China mainland, this study proposed a snow‐day direct definition method (SDDM) to determine the threshold air temperature (TAT) of rainfall and snowfall, and analysed the spatial pattern and its influential factors. The main findings include: (1) the TAT based on the SDDM varied from −1.2 to 6.3°C, with a mean value of 2.8°C for the entire study region; (2) TAT was generally higher and more variable in the low‐latitude areas, and the Qinghai‐Tibet Plateau was characterized by an abnormally high average TAT of 5.2°C, almost twice as large as that of the eastern monsoon region; (3) TAT exhibited a significant positive correlation with altitude and negative correlation with precipitation and relative humidity. The results presented in this paper have potential application for studies of large‐scale snowfall climatology and climate change, weather forecasting techniques, and hydrological model parameterization in areas with complex and diverse geographical and climatic conditions. [ABSTRACT FROM AUTHOR]

Seasonal forecasts are commonly issued in the form of anomalies, which are departures from the average over a specified multiyear reference period (climatology). The model climatology is estimated as the average of the retrospective forecasts over the hindcast period. However, different operational centers that provide seasonal ensemble predictions use different hindcast periods based on their model climatology. Additionally, the hindcast periods of recently developed and upgraded newer models have shifted in the recent years. In this paper, we discuss the recent challenges faced by APCC multi‐model ensemble (MME) operations, especially changes in the hindcast period for individual models. Based on the results of various experiments for MME prediction, we propose changing the hindcast period, which is the most appropriate solution for APCC operation. This makes the newly developed models join the MME and increases the total number of participating models, which facilitates the skill improvement of the MME prediction. Plain Language Summary: In seasonal forecasting, it is well known that the MME, which combines different single‐model predictions from various operational and research centers, is a more effective way to improve forecast skill. Since 2005, the APCC has provided the MME seasonal forecasts, and the models participating in the APCC MME operations have been continuously changing. In particular, as the hindcast periods of newly developed models shift to the latest, they cannot participate in operational MME forecasts because of climatological discrepancies. However, over time, as the number of new models expected to provide skillful forecasts gradually increases, the APCC faces the challenge of continuously reducing the number of participating models or changing the hindcast period to more recent years. Considering various aspects such as the number of participating models, skills, and climatology period, we selected the most appropriate method for APCC operation. Thus, the MME prediction skill has improved over most of the globe and seasons because of the increase in the number of participating models, particularly the inclusion of newer models. Key Points: APCC, which combines all the information from different ensemble prediction systems, recently faced challenges in hindcast period issuesThe proposed solution leads to an increase in the number of models contributing to MME prediction, particularly recently developed modelsIt shows improved skills for both temperature and precipitation predictions over most of the globe and seasons [ABSTRACT FROM AUTHOR]

Summary: The dedication of the international anaesthetic community to reducing the environmental impact of healthcare is important and to be celebrated. When this is underpinned by robust science, it has the potential to make a real difference. However, volatile anaesthetic agents have been widely promoted in the medical literature as damaging to the climate, leading to a drive to remove them from clinical practice. This is based on notional 'CO2‐equivalent' values created using the simple emission metric known as the global warming potential. Here, we assert that when proper consideration is given to the science of climate change, volatile anaesthetic gas emissions cannot be simply equated to real carbon dioxide emissions, and that their climate impact is vanishingly small. This paper gives anaesthetists a framework to make informed choices founded on climate science and calls for attention to be refocused on the urgent need to reduce the real carbon dioxide emissions associated with healthcare. [ABSTRACT FROM AUTHOR]

While UN reports indicate increasingly dire consequences of climate change, the political will to initiate rapid decarbonization is lacking, as nations fail to meet targets set by international agreements. Given these developments, this paper investigates the role of climate dread and fatalism in the discourse about climate science. We examine the treatment of fatalism in major theories of the policy process, noting that climate policy represents a relatively novel situation—in which a problem once deemed solvable is being redefined as an issue outside the realm of human control. Using automated content analysis, we examine how journalists and interest groups framed reports issued by Intergovernmental Panel on Climate Change (IPCC) from 2013 to 2022. We find limited changes in the tone and content of news stories and environmental groups' statements, and we suggest that these findings reflect under‐reaction of the political system to new information. [ABSTRACT FROM AUTHOR]

The Royal Meteorological Society's flagship publication, the Quarterly Journal of the Royal Meteorological Society (QJ), recently celebrated its 150th anniversary. To commemorate this milestone, a special collection of 22 classic papers was published, highlighting key advancements in the field from 1925 to 2020. The journal has had a wide international reach and impact, as evidenced by its geographical distribution of articles and downloads. The anniversary was also marked by a commemorative meeting, where experts discussed various topics related to meteorology, including climate science, data assimilation, accurate weather forecasting, cloud evolution, and remote sensing observations. The event provided valuable historical perspectives, practical advice on publishing, and networking opportunities for early-career researchers. [Extracted from the article]

Tackling environmental challenges that face humanity requires us to acknowledge new ways of working and to cross disciplinary boundaries. However, the methodological toolkit used by environmental researchers to explore the human attitudes, knowledge and behaviours that drive global challenges such as biodiversity loss and climate breakdown remains constrained.Here, we describe participatory video, a methodology for capturing and communicating knowledge, which goes beyond interviews, focus groups and participant observation. We draw from the literature and our own experience of conducting participatory video projects in Nepal, Guyana and Peru. We demonstrate the diverse ways in which the methodology can be applied to environmental research and highlight its strengths and limitations.Participatory video provides a more holistic understanding of environmental issues by using multiple types of data, its longer‐term engagement with issues, opening channels of communication between stakeholders, engaging a diversity of knowledge systems and advocating for transformative change.By taking a participatory video approach, environmental researchers may begin to counter commonplace criticisms about lack of diversity and entrenched colonialism. This simultaneously responds to wider calls for environmental research to engage with social justice issues, represent diverse voices, understand different contexts and acknowledge the role of power. Crucially, this helps build trust amongst all those involved.By demonstrating how we have successfully used participatory video in projects in conservation, ecology and climate science, we provide guidance for researchers looking to expand their methodological toolkit. Ultimately, we seek to improve the use of participatory methods to help support communities to tackle the environmental challenges that they face. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

The ICESat‐2 (Ice, Cloud and Land Elevation Satellite‐2) photon‐counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat‐2 measurements. Careful planning and dedicated development were accomplished during the pre‐launch phase of the mission in preparation for the 2018 launch. Once on‐orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat‐2's contributions to Earth observation science initiated by NASA and the ICESat‐2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat‐2's data capabilities and allowed for advanced science applications beyond the original mission objectives. Plain Language Summary: NASA launched its second Earth observing laser altimeter in 2018 with mission objectives of collecting observations in support of Earth science as a window into climate change impacts on our planet. Pre‐launch studies focused on specific instrument settings and on‐board data processing to support the mission objectives without violating data volume constraints. Once the instrument was on‐orbit and operational, evaluation of the algorithms for success in signal detection, signal finding, and signal telemetry was undertaken. In response to the evaluation, updates have been made to optimize the data provided by the mission. Key Points: Since the 2018 ICESat‐2 (Ice, Cloud and Land Elevation Satellite‐2) launch multiple updates have been made to the onboard flight receiver algorithm parameters to improve access and utility of the data for a multi‐disciplinary science communityThe adjustments have been primarily in the in the vertical telemetry band settings and have facilitated enhanced observations of blowing snow and increased detection opportunities of bathymetry in nearshore environmentsThe parameter changes have been made to mitigate data losses in certain situations and to advance science applications outside of the primary science objectives of the ICESat‐2 mission [ABSTRACT FROM AUTHOR]

Anomalous tropical longwave cloud‐radiative heating of the atmosphere is generated when convective precipitation occurs, which plays an important role in the dynamics of tropical disturbances. Defining the observed cloud‐radiative feedback as the reduction of top‐of‐atmosphere longwave radiative cooling per unit precipitation, the feedback magnitudes are sensitive to the observed precipitation data set used when comparing two versions of Global Precipitation Climatology Project, version 1.3 (GPCPv1.3) and the newer version 3.2 (GPCPv3.2). GPCPv3.2 contains larger magnitudes and variance of daily precipitation, which yields a weaker cloud‐radiative feedback in tropical disturbances at all frequencies and zonal wavenumbers. Weaker cloud‐radiative feedbacks occur in GPCPv3.2 at shorter zonal lengths on intraseasonal timescales, which implies a preferential growth at planetary scales for the Madden‐Julian oscillation. Phase relationships between precipitation, radiative heating, and other thermodynamic variables in eastward‐propagating gravity waves also change with the updated GPCPv3.2. Plain Language Summary: High‐altitude, widespread anvil clouds are generated when heavy convective precipitation occurs in the tropics. These clouds are not only a passive product produced by convection, but they also can subsequently enhance convection by trapping upward infrared radiative flux emitted by the Earth, effectively heating the atmosphere. This additional radiative heating effect can induce upward motion in the tropics, supporting the convective systems by transporting more humid air from below. However, the strength of this cloud‐radiative feedback is hard to estimate because global, continuous observations of surface precipitation are difficult to derive. In this study, the strength of the radiative feedback is calculated using the same product of observed radiative heating against two different observational precipitation products. A newer improved precipitation product yields much weaker radiative feedback strengths for all types of tropical weather systems. In addition, cloud‐radiative heating is found to substantially lag behind precipitation in certain fast, eastward‐propagating tropical rainfall systems in the newer precipitation product, unlike the older one. Why such a lag exists is unclear. The discrepancy of the estimation of cloud‐radiative feedback strengths and properties in the older versus the newer precipitation products indicates that our understanding of mechanisms supporting tropical disturbances is still incomplete. Key Points: The updated Global Precipitation Climatology Project (GPCP) precipitation product has more frequent high rain rates, yielding a weaker longwave radiative feedbackThe updated radiative feedback supports less moistening of the Madden‐Julian oscillation, but imposes stronger planetary scale selectionThe phase relationship between precipitation and thermodynamic fields in eastward‐propagating tropical waves are sensitive to GPCP versions [ABSTRACT FROM AUTHOR]

A leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more expensive are the ensembles needed to capture accurate statistics in the tail of the distribution. Here, we focus on events that are localized in space and time, such as heavy precipitation events, which can start suddenly and decay rapidly. We advance a method for sampling such events more efficiently than straightforward climate model simulation. Our method combines elements of two existing approaches: adaptive multilevel splitting (AMS), a rare event algorithm that generates rigorous statistics but fails to enhance the sampling of sudden, transient extremes; and "ensemble boosting," which generates physically plausible storylines of these events but not their statistics. We modify AMS by splitting trajectories well in advance of the event's onset, following the approach of ensemble boosting. Early splitting requires a rejection step that reduces efficiency, but it is critical for amplifying and diversifying simulated events in tests with the Lorenz‐96 model, for which we demonstrate improved sampling of extreme local energy fluctuations by approximately a factor of 10 relative to direct sampling. Our method is related to previous algorithms, including subset simulation and anticipated AMS, but is distinctly tailored to handle bursting events caused by chaotic traveling waves. Our work makes progress toward the goal of efficiently sampling such transient local extremes in atmospheric models. Plain Language Summary: What is the strongest rainstorm that we can expect in a given thousand‐year period? To augment the available ∼100 years of historical data and to account for climate change, computer simulations are a useful, but expensive, tool to answer such questions. A model must run for many millennia to deliver an answer with statistical confidence. Rare event algorithms provide a promising alternative simulation protocol, in which an ensemble of short simulations is biased to produce more extreme events and reweighting is used to correct for the bias when calculating statistics. However, a classical rare event algorithm fails when the events of interest are short and "bursty" (like heavy rainstorms) instead of long and slow‐moving (like anomalously hot summers). We modify the rare event algorithm to make it amenable to events resembling heavy precipitation in an idealized dynamical system with chaotic traveling waves. Key Points: Rare event algorithms may help address the challenge of simulating extreme weather events and quantifying their probabilityWhen the event of interest is sudden and transient, perturbed ensembles diversify too slowly for standard rare event algorithms to workUsing the Lorenz‐96 model as a prototype for midlatitude weather, we use early perturbation and a rejection step to gain a speedup [ABSTRACT FROM AUTHOR]

Emulators, or reduced complexity climate models, are surrogate Earth system models (ESMs) that produce projections of key climate quantities with minimal computational resources. Using time‐series modeling or more advanced machine learning techniques, data‐driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state‐of‐the‐art ESMs. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data‐driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (a) enjoys the flexibility of statistical machine learning models and can learn from data, and (b) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform EBMs, while its robust physical foundation safeguards against the pitfalls of purely data‐driven models. We also illustrate how FaIRGP can be used to obtain estimates of top‐of‐atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data‐driven methods in climate emulation. Plain Language Summary: Emulators are simplified climate models that can be used to rapidly explore climate scenarios—they can run in less than a minute on an average computer. They are key tools used by the Intergovernmental Panel on Climate Change to explore the diversity of possible future climates. Data‐driven emulators use advanced machine learning techniques to produce climate predictions that look very similar to the predictions of complex climate models. However, they are not easy to interpret, and therefore to trust in practice. In this work, we introduce FaIRGP, a data‐driven emulator based on physics. The emulator is flexible and can learn from data to improve its predictions, but is also grounded on physical energy balance relationships, which makes it robust and interpretable. The model performs well in predicting future global and local temperatures under realistic future scenarios, outperforming purely physics‐driven or purely data‐driven models. Further, the probabilistic nature of our model allows for mathematically tractable uncertainty quantification. By gaining trust in such a data‐driven yet physically grounded model, we hope the climate science community can benefit more widely from their potential. Key Points: We introduce FaIRGP, a Bayesian machine learning emulator for global and local mean surface temperatures that builds upon a physically based simple climate modelThe model improves upon both purely physically‐driven and purely data‐driven baseline emulators on several metrics across realistic future scenariosThe model is fully mathematically tractable, which makes it a convenient and easy‐to‐use probabilistic tool for the emulation of surface temperatures, but also for downstream applications such as detection and attribution or precipitation emulation [ABSTRACT FROM AUTHOR]

Guided by family systems and achievement goal theories, this study examined how the sex of athletes and their main sport parents, as well as sport participation patterns (same sport, different sports, and no sports) of parent–athlete and sibling sex compositions (same‐sex and mixed‐sex), differentiated athlete perceptions of parenting climates—task‐involving (emphasizing individual improvements, effort, and mastery) and ego‐involving (emphasizing winning and performance comparison). Participants were 353 U.S. high school athletes (Mage = 15.52 and SD = 1.18; 55% male) who completed a survey on perceived parenting climates, family compositions, and sport backgrounds of their parents and siblings. We conducted six moderated regression analyses, two of which used (1) athlete sex and main sport parents' sex, (2) sport participation patterns of parent–athlete sex compositions, or (3) sport participation patterns of sibling sex compositions as independent variables. Four of the analyses were statistically significant with small effect sizes, showing that (1) boys perceived greater ego‐involving climates than girls; (2) athletes whose same‐sex parents played sports (same or different sports) compared to no sports‐perceived greater task‐involving climates: (3) athletes whose mixed‐sex parents played (same or different sports) compared to no sports‐perceived greater task‐involving climates and less ego‐involving climates; and (4) athletes whose mixed‐sex siblings played different sports than they did, compared no sports, and perceived greater task‐involving climates. None of the interactions were significant. Findings provide theoretical and practical implications by incorporating motivational climates, addressing the potential relationships of parents' and mixed‐sex siblings' sport participation to adaptive parenting climates. Highlights: Family system, including parents and siblings, and their sex compositions are important factors to consider when examining athlete perceptions of parenting.Athletes perceived greater task‐involving when their same‐sex or mixed‐sex parents played sports rather than no sports and less ego‐involving climates when their mixed‐sex parents played sports rather than no sports.Athletes perceived greater task‐involving climates when their mixed‐sex siblings played different sports than they did rather than no sports. [ABSTRACT FROM AUTHOR]

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long‐term adaptation strategies, and guide mitigation approaches. Increasingly complex socio‐economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high‐resolution modeling of global‐to‐local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science‐to‐service pathways, and co‐production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally‐funded U.S. modeling groups outline here perspectives on a six‐pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services. Plain Language Summary: In the face of a changing climate, the understanding, predictions and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long‐term adaptation strategies, and guide mitigation approaches. Increasingly complex socio‐economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities currently at hand, and working with stakeholders to co‐produce the information that they require. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally‐funded U.S. modeling groups outline here perspectives on a six‐pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support the next generation of climate services. Key Points: Facing the uncharted territory of a changed climate and increasingly complex socio‐economic systems requires the best possible predictionsScientific and technological opportunities are at hand to accelerate progress on U.S. climate predictions and projectionsCapitalizing on the most transformative opportunities calls for an unprecedented level of coordination and resources [ABSTRACT FROM AUTHOR]

Global climate change is often thought of as a steady and approximately predictable physical response to increasing forcings, which then requires commensurate adaptation. But adaptation has practical, cultural and biological limits, and climate change may pose unanticipated global hazards, sudden changes or other surprises–as may societal adaptation and mitigation responses. These poorly known factors could substantially affect the urgency of mitigation as well as adaptation decisions. We outline a strategy for better accommodating these challenges by making climate science more integrative, in order to identify and quantify known and novel physical risks including those arising from interactions with ecosystems and society. We need to do this even–or especially–when they are highly uncertain, and to explore risks and opportunities associated with mitigation and adaptation responses by engaging across disciplines. We argue that upcoming climate assessments need to be more risk‐aware, and suggest ways of achieving this. These strategies improve the chances of anticipating potential surprises and identifying and communicating "safe landing" pathways that meet UN Sustainable Development Goals and guide humanity toward a better future. Plain Language Summary: Global risks may arise from many places, not all of which are being considered at the moment in the usual projections of future climate. Some risks are being overlooked, sometimes by most of the physical science community itself, or else by others who need information about future climate. We think physical climate sciences can do a lot to remedy this situation by looking more systematically at a broader array of risks and possible futures, narrowing them down, and collaboratively seeking better ways to express them. In this perspective we argue why this is important and explain a few ways it can be done. Key Points: Climate science needs to adjust to account systematically for low‐likelihood, high‐impact risks and comprehensive pathway interactionsAchieving this requires new strategies; several are outlined here, including increased model coupling and scenario explorationSuccess will require collaboration between physical sciences and other disciplines [ABSTRACT FROM AUTHOR]

Climate change adaptation planning at the municipal level has become mandatory due to the increasing frequency of extreme weather and climate events. The availability of near‐future climate data is the first step towards creating an adaptation plan for a city. We created the CP_OdU (Climate Projections for Odesa, Ukraine) dataset, which contains daily output variables from the 102 model runs and monthly (yearly) indices calculated for 2021–2050 in the closest to Odesa land‐located model grid point. The future data are based on 26 and 76 simulations for the scenarios RCP4.5 and RCP8.5, respectively, of 14 RCMs from the EURO‐CORDEX project. The horizontal resolution of spatial grids in the RCMs is ~0.11° or ~12 km. The CP_OdU dataset contains 76 indices relating to cloudiness, wind parameters, relative humidity, precipitation amount, snow depth and temperature. A very short description of the near‐future climate in Odesa for the RCP8.5 scenario shows its trend towards a Mediterranean climate. The rising temperature supported by the change of intra‐annual variations of precipitation will result in hot, dry summers and mild, moderately wet winters. The CP_OdU dataset can be used by climate scientists, applied science engineers and climate stakeholders in society for the creation of a climate change adaptation plan for Odesa, Ukraine. [ABSTRACT FROM AUTHOR]

To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science. Plain Language Summary: While temperatures around the world continue to warm due to human‐caused climate change, some areas have observed smaller temperature trends than others. Understanding this regional variability in the rate of warming is important when assessing future projections. One location that has observed less warming is across the United States during their summer season. To evaluate temperature variability in this region using real‐world observations and climate model simulations, we use a statistical method from artificial intelligence called neural networks. The goal of the neural network setup is to learn temperature patterns across the United States and then identify whether climate change effects have exceeded the range of natural variability that has occurred in the past. This is called the timing of emergence (ToE), which is the first year that the effect has clearly appeared. We find that the average United States minimum temperature increase has already emerged in historical records. However, we find no ToE for the average maximum temperature, other than in the Western United States. Another important finding of this study is that by using higher resolution climate model data (i.e., more latitude and longitude points), we find better accuracy in the neural network predictions. Key Points: Forced temperature changes have emerged in observations during summer in the United States as detected by an artificial neural networkIncreasing spatial resolution improves neural network skill for predicting the year of a given summer temperature mapWestern United States land surface climate properties contribute to earlier timing of emergence predictions for the SPEAR climate model [ABSTRACT FROM AUTHOR]