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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]

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]

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]

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]

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]

As the Arctic and its rivers continue to warm, a better understanding of the possible future impacts on people would benefit from close partnership with Indigenous communities and scientists from diverse fields of study. We present efforts by the Arctic Rivers Project to conduct community‐engaged research to increase collective understanding of the historical and potential future impacts of climate change on rivers, fish, and Indigenous communities. Working in central to northern Alaska and the Yukon Territory in Canada, the project seeks to engage with Indigenous communities in ethical and equitable ways to produces science that is useful, useable, and used that may serve as an example for future research efforts. Toward this goal, we formed an Indigenous Advisory Council and together developed project‐specific knowledge co‐production protocols. This paper provides a novel model of design and implementation to co‐produce knowledge with communities across a large study domain. Plain Language Summary: The Arctic and rivers located in the Arctic and subarctic are warming due to climate change. To understand the impacts this warming will have on people, partnering with impacted Indigenous communities in the region is important. It is also important that these partnerships are ethical and equitable and produce science that is actionable. This paper discusses efforts undertaken by a specific project, the Arctic Rivers Project, to conduct ethical and equitable research with Indigenous communities and generate science that is useful to those communities. Through this research our goal is to better understand potential future impacts of climate change on rivers, fish, and Indigenous communities in central northern Alaska and the Yukon Territory in Canada. To achieve this goal, the project formed an Indigenous Advisory Council (IAC) and together developed guidelines for how we can work collaboratively with Indigenous communities. Our specific process of forming an IAC and guidelines is, to our knowledge, a new way to approach collaborative research when working across a large geographic area. We present our process here so that it may provide an example for other research efforts. Key Points: Arctic climate information can be made useful, useable, and used by equitably accounting for diverse community adaptation needs through knowledge co‐productionInstitutional and community capacity, including means and ability, is necessary for equitable knowledge co‐production to occurWe present an approach for co‐producing knowledge with Indigenous communities that can serve as an example for other scientific efforts [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]

Adaptation decision‐scientists increasingly use real‐option analysis to consider the value of learning about future climate variable development in adaptation decisions. Toward this end learning scenarios are needed, which are scenarios that provide information on future variable values seen not only from today (as static scenarios), but also seen from future moments in time. Decision‐scientists generally develop learning scenarios themselves, mostly through time‐independent (stationary) or highly simplified methods. The climate learning scenarios thus attained generally only poorly represent the uncertainties of state‐of‐the‐art climate science and thus may lead to biased decisions. This paper first motivates the need for learning scenarios by providing a simple example to illustrate characteristics and benefits of learning scenarios. Next, we analyze how well learning scenarios represent climate uncertainties in the context of sea level rise and present a novel method called direct fit to generate climate learning scenarios that outperforms existing methods. This is illustrated by quantifying the difference of the sea level rise learning scenarios created with both methods to the original underlying scenario. The direct fit method is based on pointwise probability distributions, for example, boxplots, and hence can be applied to static scenarios as well as ensemble trajectories. Furthermore, the direct fit method offers a much simpler process for generating learning scenarios from static or "ordinary" climate scenarios. Plain Language Summary: Many climate change adaptation decisions require large investments in infrastructure (e.g., dikes), while at the same time future projections about critical variables (e.g., sea level rise) are highly uncertain. Decision‐scientists address these challenges with methods based on flexibility and staged decision‐making. For example, a coastal decision‐maker could implement a dike with a wider foundation, and, if necessary, upgrade the dike height in the future. The decision‐maker will learn by observing future sea level rise if higher dike protection levels are actually necessary in the future. In order to assess whether it is economically beneficial to wait for future learning through observations, and thus to justify additional expenses for flexible infrastructure investments, learning scenarios are required. Learning scenarios provide projections of critical variables seen from today and from future moments in time. For example, learning scenarios of sea level rise contain sea level rise projections seen from 2050 onward, depending on a certain amount of sea level rise observed until 2050. In this paper, we provide a simple example to illustrate coastal decision‐making with a learning scenario, propose a new method to generate learning scenarios, and apply this method to generate sea level rise learning scenarios. Key Points: We show how climate learning scenarios can be applied for improving and justifying investments in flexible long‐lasting infrastructureWe develop sea level rise learning scenarios based on Intergovernmental Panel on Climate Change sixth Assessment Report using a novel method termed direct fitOur new method reduces the average deviation of learning scenarios from the original data by 83% compared to standard methods [ABSTRACT FROM AUTHOR]

An important component of the risks from climate change arises from outcomes that are very unlikely, but whose impacts if they were to occur would be extremely severe. Examples include levels of surface warming, or changes in the water cycle, that are at the extreme of plausible ranges, or crossing of a climate system "tipping point" such as ice sheet or ocean circulation instability. If such changes were to occur their impacts on infrastructure or ecosystems may exceed existing plans for adaptation. The traditional approach of ensemble climate change projections is not well suited to managing these High Impact‐Low Likelihood (HILL) risks, where the objective is to "prepare for the worst" rather than to "plan for what's likely." In this paper we draw together a number of ideas from recent literature, to classify four types of HILL climate outcome and to propose the development of a practical "toolkit" of physical climate information that can be used in future to inform HILL risk management. The toolkit consists of several elements that would need to be developed for each plausible HILL climate outcome, then deployed individually to develop targeted HILL risk management approaches for individual sectors. We argue that development of the HILL toolkit should be an important focus for physical climate research over the coming decade, and that the time is right for a focused assessment of HILL risks by the Intergovernmental Panel on Climate Change in its 7th Assessment Cycle. Plain Language Summary: To prepare for the risks that arise from climate change (and avoid them where possible), it is important to understand how climate is likely to change in future, and what the impacts are likely to be. Over many years, climate science has developed sophisticated climate projections to estimate these likely impacts, and these are widely used to plan how people and societies will need to adapt to climate change. However it is also important to understand possibilities that are unlikely, but would have even more severe impacts if they did occur—for example, global warming levels at the high end of plausible estimates, or crossing a "tipping point" for major changes in ice sheets or ocean currents. A different type of information is needed to plan for these risks. In this paper we propose a new set of climate information "tools" to respond to these high‐impact risks. The tools include plausible scenarios of extreme outcomes, and early warning systems to detect if they are on the horizon. Combining these new tools with existing climate projections will allow society to understand more fully the risks of climate change, and to plan for the likely effects while preparing for the worst. Key Points: Climate outcomes or events that have a high impact are a key component of climate risk, even if their likelihood is lowTraditional climate projections are of limited use to inform management of high impact‐low likelihood risksPhysical climate science needs an increased focus on storylines, early warning and monitoring to inform management of high impact risks [ABSTRACT FROM AUTHOR]

While the majority of adults across the United States report that they believe that global warming is happening, far fewer report discussing global warming (Howe et al., 2015, https://doi.org/10.1038/nclimate2583; Marlon et al., 2022, https://climatecommunication.yale.edu/visualizations‐data/ycom‐us/). One way to inspire further climate action and engagement is to increase individuals' capacity to confidently and effectively discuss climate change. Climate communication science highlights that such communication is most effective when it is anecdotal, narrative, tailored to the audience, and place‐based. To generate climate conversations and inspire action in a variety of communities, partners at the University of Minnesota Extension piloted a program to train community members from across the state of Minnesota in effective climate communication through a series of instructional workshops, coaching, and participant‐led communication activities. Following the training portion of the program, participants identified and hosted their own climate‐related communication activities in their communities. These "climate conversations" took place across Minnesota and included community events, dialogue with elected leaders, and conversations stimulated through literature, among other activities. In their communities, program participants sparked conversations, initiated long‐term climate action efforts, and improved their sense of efficacy in response to climate change. Participants also reported that they improved their climate conversation skills, increased their local climate knowledge, established a support network with fellow participants, had reduced anxiety around communicating, and increased their confidence in being able to communicate about climate change in their communities. This pilot program provides a framework for future cohort‐ and community‐based climate communication programs in the state and beyond. Plain Language Summary: More frequent, effective climate conversations initiated by a diversity of trusted voices can help to increase climate concern and desire for action at the community level. However, in the United States, there is a disconnect between the level of concern individuals have about climate change and the extent to which individuals talk about the issue. To help bridge this gap, the University of Minnesota Climate Adaptation Partnership developed a training program aimed at inspiring and equipping local community members across Greater Minnesota with the skills and confidence to have effective conversations about climate change in their communities. This paper summarizes the programmatic activities we used to support our goals, and some reflections on the program's results. This pilot program provides a framework for future efforts that can be facilitated by Extension programs, community‐based organizations, universities, and others to inspire and accelerate similar community‐centered climate conversations. Key Points: We piloted a training program to teach Minnesotans to effectively communicate about climate change and its impacts on their communitiesParticipants improved behavioral and conversation skills to communicate about climate change more effectivelyThis program provides a framework for future programs aimed at increasing the diversity of people engaging in local climate conversations [ABSTRACT FROM AUTHOR]

Broadband (spectrally‐integrated) radiation calculations are dominated by the expense of spectral integration, and many applications require fast parameterizations for computing radiative flux. Here we describe a novel approach using a linear weighted sum of monochromatic calculations at a small set of optimally‐chosen frequencies. The empirically‐optimized quadrature method is used to compute atmospheric boundary fluxes, net flux profiles throughout the atmosphere, heating rate profiles, and top‐of‐the‐atmosphere forcing by CO2, in the longwave for clear skies. We evaluate the method against two modern correlated k‐distribution models and find that we can achieve comparable errors with 32 spectral points. We also examine the effect of minimizing different cost functions, and find that in order to accurately represent heating rates and CO2 forcing, these quantities must be included in the cost function. Plain Language Summary: Quantifying the way radiation flows through the atmosphere is computationally expensive, and most applications require fast approximation. In this paper, we develop an empirically‐optimized quadrature method that can compute quantities of interest in climate science and weather prediction such as radiative flux throughout the atmosphere, heating rates, and forcing by carbon dioxide. The method uses a simple optimization algorithm, combined with a linear model, to achieve accuracy and computational cost of similar order of magnitude to the modern correlated k‐distribution models. Depending on the focus of the application, the algorithm can easily be adapted to prioritize desired quantities. Key Points: Spectrally integrated radiative quantities can be estimated from a small weighted set of monochromatic calculationsTradeoffs between computational cost and accuracy are commensurate with correlated k‐distribution modelsOptimization priorities can be tailored to a specific applications [ABSTRACT FROM AUTHOR]

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]

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]

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This paper sheds light on an important and under‐researched issue: The sources of information about climate change that teachers use. Utilising a 'scoping review' methodological approach, we analysed over 600 papers to address two main questions: What sources of information about climate change are teachers using? In what ways are teachers using these sources of information? Through our use of inclusive search terms and detailed analysis of papers, we found only 13 studies of relevance, none of which primarily focus on the sources of information teachers use. The 13 studies are all located in the Global North, and within this nearly half are in the USA. Methodologically, all apart from two rely on teachers' reports rather than observation or other methods. Four types of sources of information were frequently mentioned: The Internet; government sources; mass media and professional development courses. The 'superabundance' of information now available to teachers (particularly online), the importance of high‐quality information for students' understandings of climate change, and the limited research on the sources of information about climate change that teachers use makes this is a significant blind spot for research to address. [ABSTRACT FROM AUTHOR]

In recent years, the US and the Netherlands have been on opposing sides of the spectrum regarding climate litigation. Dutch courts, in several revolutionary climate cases, have been an arena of societal change, whilst climate claims in the US have been largely unsuccessful. In a way this difference seems strange, because the US judiciary has the power of constitutional review, whilst the Dutch judiciary does not. Against that background, this paper extensively compares the doctrines of judicial competence regarding political questions in both jurisdictions. As a comparative framework, this paper uses three judicial phases, namely: the institutional phase, the substantial phase, and the remedial phase. Climate litigation reveals that the Dutch doctrine of judicial competence is focused on the substantial and remedial phases, which has allowed it more freedom in reviewing climate litigation. On the other hand, climate litigation reveals that the US doctrine of judicial competence is focused on a strict institutional phase, dominated by the Political Question Doctrine (PQD). The main contribution of this paper to the constitutional debate is that climate litigation reveals fundamental differences in doctrines of judicial competence. This is not only an important takeaway for future climate litigation, but also, in terms of the Radbruch formula, for other potential gaps between the executive and justice. [ABSTRACT FROM AUTHOR]

This study establishes a new blocking climatology in the Southern Hemisphere (SH) by using the improved detection method with the reanalysis data from 1979 to 2016. Furthermore, it investigates the dynamical linkage between SH blocking and ENSO phases. It is found that the ENSO warm phase is more conducive to the blocking formation than the cold phase while the blocking intensity is not affected by the ENSO phases. In addition, blocking in ENSO warm phase is characterized with upper‐level divergence anomaly and the cold phase with convergence anomaly equatorward of blocking onset region. Through the diagnostic analysis of tropopause divergence for the composite blocking events via an improved divergence tendency equation, the study shows that the ageostrophic effect exhibits dual effects, namely, it induces anticyclonic ageostrophic vorticity favoring the formation of blocking whereas it results in convergence anomaly close to the blocking onset region thus inhibiting blocking locally. In addition, the nonlinear term in the divergence tendency equation is found to be a dominant forcing for favoring blocking onset. Nevertheless, despite the primary contributions from the ageostrophic effect and nonlinear term to blocking formation, they could barely explain the SH blocking onset preference in ENSO warm phase. Finally, through the localized Eliassen‐Palm flux this study reveals that in ENSO warm phase the composite blocking onset region and the immediate upstream region are characterized by more localized EP flux convergence, thereby leading to more effective westerly wind weakening and favoring the blocking formation. Plain Language Summary: Previous research shows that in the Southern Hemisphere (SH), the ENSO warm phase is more conducive to the blocking formation than the cold phase, but the dynamic reasons are rarely studied. Starting from the tropopause divergence with the improved divergence tendency equation and tropical Rossby wave propagation associated with midlatitude wave‐flow interaction, this paper studies the connection between the tropics and extratropics from these two perspectives. Finally, this study reveals that the ENSO warm phase is more conducive to the blocking formation than the cold phase in the SH. Key Points: New blocking climatology is established in the Southern Hemisphere (SH) using an improved detection methodThe ageostrophic wind plays a dual effect in the SH blocking formationENSO warm phase is more conducive to blocking formation in the SH with more eddy convergence [ABSTRACT FROM AUTHOR]

The observation facts of trend and variability are estimated for a century‐long timescale, as well as some possible attributions affecting temperature extremes over Beijing‐Tianjin‐Hebei region. In this paper, the new constructed daily maximum and minimum temperature series during the last 100 years in situ stations of Beijing, Tianjin and Baoding city are used, and two 30‐year baseline climatology of 1961–1990 and 1980–2010 are both used in calculating extreme temperature events. The results indicate that there are long‐lasting, frequent and serious extreme warming over Beijing‐Tianjin‐Hebei region, especially for extreme low temperature, with larger declining trends (at 5% significance) from 1981 to 2010 baseline than those from 1961 to 1990 baseline. And from a statistical perspective, El Niño decay years and surface sea temperature (SST) anomalies at some typical ocean regions of the Arabian Sea and the Bengal Bay on the northern Indian Ocean, the Western Pacific and the Mid Atlantic, Southern Indian Ocean and around 45°–85°E, 68°–82°N on the southwest Arctic Ocean can be used as the warning signals denoting extreme warming over Beijing‐Tianjin‐Hebei region. Additionally, urbanization has a significant (at 5% significance) effect on extreme warming over Beijing‐Tianjin‐Hebei region since 1979, with 24.3% warming contribution for annual minimum temperature. [ABSTRACT FROM AUTHOR]

There are two key questions consistently raised by children and youth since the onset of the school strikes for climate in 2018 in the Global North: Why study for a future, which may not be there? Why spend a lot of effort to become educated, when our governments are not listening to the educated? (conf. fridaysforfuture.org, n.d). While the same questions are shared by young climate activists from the Global South, there are other interrelated economic realities that echo the disappointment, anger, disillusionment, desperation, and frustration driving those utterances in their socio‐political contexts. Comparative insights from so‐called 'developing' countries disclose that institutionalized schooling, commonly confused with education, manipulates the aspirations of younger generations and their wider societies. Contemporary global schooling is one of the key propellers of global economic agendas geared towards producing human capital that is 'employable' in the future job market. The paradox of a global education agenda geared towards generating human capital employable on a job market is that most of those jobs (if at all they will be there) continue serving the very economic system that is threatening the right to life, health, culture (especially for indigenous communities) and the best interests of future generations on this planet. As young climate activists from various countries collectively argue within the framework of the United Nations Convention of the Rights of the Child, major economies are failing to protect the basic human rights of children and future generations. I suggest that the various interrelated crises evident through the key questions raised by young climate activists must be considered as part of a North–South continuum. In this paper, I reflect upon these interrelated crises by developing a decolonial childist approach to education—understood as an intergenerational relationship, implying an effort on part of present adults becoming good ancestors of the future, in the present. Such efforts require larger structural shifts which I consider by means of the four strategies I discuss building upon previous co‐reflections. [ABSTRACT FROM AUTHOR]

Background: Information about airborne pollen concentrations is required by a range of end users, particularly from the health sector who use both observations and forecasts to diagnose and treat allergic patients. Manual methods are the standard for such measurements but, despite the range of pollen taxa that can be identified, these techniques suffer from a range of drawbacks. This includes being available at low temporal resolution (usually daily averages) and with a delay (usually 3–9 days from the measurement). Recent technological developments have made possible automatic pollen measurements, which are available at high temporal resolution and in real time, although currently only scattered in a few locations across Europe. Materials & Methods: To promote the development of an extensive network across Europe and to ensure that this network will respond to end user needs, a stakeholder workshop was organised under the auspices of the EUMETNET AutoPollen Programme. Participants discussed requirements for the groups they represented, ranging from the need for information at various spatial scales, at high temporal resolution, and for targeted services to be developed. Results: The provision of real‐time information is likely to lead to a notable decrease in the direct and indirect health costs associated with allergy in Europe, currently estimated between €50–150 billion/year.1 Discussion & Conclusion: A European measurement network to meet end user requirements would thus more than pay for itself in terms of potential annual savings and provide significant impetus to research across a range of disciplines from climate science and public health to agriculture and environmental management. [ABSTRACT FROM AUTHOR]

In 2021, Bangladesh reached 97% nation‐wide electricity access. With over 4.1 million solar home systems (SHS) installed by 60 Partner Organisations (POs) providing renewable electricity to more than 22 million rural people, SHS and microfinance have been instrumental in bridging and achieving the country's universal access goal. However, a huge gap remains in achieving the country's 100% use of renewable energy by 2050, with only 3.1% of current electricity generation accounted for by renewables. While at centralised level, governmental institutions are struggling to move past the legacy mandate of grid extension to an energy transformation paradigm at decentralised levels, rural Bangladeshi villages are already leading this transformation. Today, 100 solar peer‐to‐peer microgrids, built following the swarm electrification pathway, allow end‐users to run appliances, charge vehicles, and to trade renewable electricity for income. In this paper, we describe the organisational model of the partnership between Grameen Shakti, Bangladesh's largest PO, and climate‐tech start‐up ME SOLshare, the swarm electrification implementer. We assess, using data from the peer‐to‐peer grids, their socio‐economic and environmental impact, and their contribution to the SDG 2030 Agenda. Finally, we give an outlook towards the potential of achieving sustainable energy development through a scale‐up of the swarm electrification approach, including under a scenario of 100% national grid electrification. [ABSTRACT FROM AUTHOR]

In this paper we present daily mean wind speed as a new variable in the publicly accessible E‐OBS gridded datasets. Each of the variables in the E‐OBS dataset is provided at a spatial resolution of 0.1° × 0.1° and 0.25° × 0.25°. The variable "daily mean wind speed" starts from 1980 and is updated every month. The main method that we apply is the use of adaptive covariate selection to model the monthly mean wind speed as a function of covariates, like altitude, distance to coast and surface roughness. Then, we use Gaussian process regression to interpolate the daily anomalies from the station locations to the grid locations. In addition, we develop ensemble dispersion improvement auto‐tune (EDIT) to improve the reliability of the local ensemble spread. In order to communicate the methodological uncertainty in the resulting dataset, the E‐OBS wind speed grid is provided as a 20‐member ensemble of random realizations. As part of a preliminary analysis, we investigate the hypothesized wind speed stilling effect in recent decades. Indeed, we do find a small yet significant wind speed stilling effect in the E‐OBS dataset. [ABSTRACT FROM AUTHOR]

Three recently published papers including Napier et al. (2022, https://doi.org/10.1029/2021PA004355) utilize novel microanalytical approaches with varved marine sediments to demonstrate the potential to reconstruct seasonal and inter‐annual climate variability. Obtaining paleoclimate data at a resolution akin to the observational record is vitally important for improving our understanding of climate phenomena such as monsoons and modes of variability such as the El Niño Southern Oscillation, for which appraisals of past inter‐annual variability is critical. The ability to generate seasonal and inter annual resolution sea surface temperature proxy time series spanning a thousand years or more is revolutionary and has the potential to fill gaps in our knowledge of climate variability. Although generally limited to sediments from regions with oxygen depleted bottom waters, there is great potential to integrate shorter seasonal resolution climate "snap shots" from other archives such as annually banded corals into composite time series. But as paleoceanographic data are used more by the observational and modeling fields, we make the case for conducting a thorough case‐by‐case assessment of the processes that influence the climate signal recovered from proxies, using careful replication to validate new approaches. Understanding or exploring the potential influence of processes which effectively filter the climate signal will lead to more quantitative paleoceanographic data that will better serve the broader climate science community. Plain Language Summary: Year to year differences in the strength of climate phenomena like monsoons and El Niño drive droughts and floods with dramatic consequences for human society. The longest records of measurements of climate generally only extend back to the 1800s, meaning we have a very short period of data to assess the changing climate and test climate model predictions. To obtain longer records of climate we use proxies which relate a variable we can measure in an archive such as the width of growth bands of coral skeletons to a climate observation such as the temperature of the sea surface. Usually, proxy climate records from marine sediments would provide sea surface temperature (SST) values that are the average of some 100 or 1,000 years. However, a new technique to measure climate proxies at sub mm resolution now allows for marine sediments, especially those that accumulate distinctive dark and light layers with changing seasons, to be used in principle to produce SST proxy data for individual seasons and years. This enables records of year‐to‐year differences that extend for thousands of years to be created. These records will undoubtedly revolutionize our understanding of the climate system and how climate proxies work. Key Points: Novel micro‐analytical techniques allow seasonally resolved climate proxy data from varved marine sedimentsPotential to generate seasonal and inter annual resolution sea surface temperature proxy time series spanning >1,000 yearsThorough assessment of processes that influence the climate signal recovered from proxies, validated with careful replication, is required [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]

Version 5 (v05) of the thermospheric wind data from the Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) instrument on the Ionospheric Connection Explorer (ICON) mission has been recently released, which largely avoids local‐time dependent artificial baseline drifts that are found in previous versions of the ICON/MIGHTI wind data. This paper describes monthly climatologies of zonal‐mean winds and tides based on the v05 ICON/MIGHTI data under geomagnetically quiet conditions (Hp30 < 3o) during April 2020–March 2022. Green‐line winds in the lower thermosphere (90–110 km) and red‐line winds in the middle thermosphere (200–300 km) are analyzed, as these data cover both daytime and nighttime. The latitude and height structures of zonal‐mean winds and tides are presented for each month, and the results are compared with the widely used empirical model, Horizontal Wind Model 2014 (HWM14). The ICON/MIGHTI and HWM14 results are in general agreement, providing a validation of the v05 ICON/MIGHTI data. The agreement is especially good for the zonal‐mean winds. Amplitudes of lower thermospheric tides from ICON/MIGHTI tend to be larger than those from HWM14 as well as from an empirical model, Climatological Tidal Model of the Thermosphere (CTMT). This could be due to the influence of interannual variability of the tides. The amplitude structure of lower thermospheric tides in HWM14 does not match those from ICON/MIGHTI and CTMT in some months. Also, HWM14 underestimates the meridional‐wind amplitude of the migrating diurnal tide in the middle thermosphere. These results highlight the need for improved tidal representation in HWM14. Key Points: Monthly climatologies of zonal‐mean winds and tides at 90–110 km and 200–300 km are determined using v05 Ionospheric Connection Explorer/Michelson Interferometer for Global High‐resolution Thermospheric Imaging (ICON/MIGHTI) observationsICON/MIGHTI and Horizontal Wind Model 2014 results are in general agreement, providing a validation of the Version 5 ICON/MIGHTI dataThe agreement is especially good for the zonal‐mean winds, while some discrepancies are found in tidal amplitudes [ABSTRACT FROM AUTHOR]

Global response to climate‐sensitive infectious diseases has been uncertain and slow. The understanding of the underlying vulnerabilities which forms part of changes created by forces within the Earth system has never before been critical until the coronavirus disease 2019, "COVID‐19" pandemic with the initial developmental phase linked to weather elements and climate change. Hence, the heightened interest in climate‐sensitive infectious diseases and GeoHealth, evident in the renewed calls for "One Health" approach to disease management. "One Health" explains the commonality of human and animal medicine, and links to the bio‐geophysical environment, yet are at crossroads with how forces within the Earth system shape etiologies, incidences, and transmission dynamics of infectious diseases. Hence, the paper explores how these forces, which are multistage and driven by climate change impacts on ecosystems affect emerging infectious diseases, leading to the question "what drive the drivers of diseases?" Three questions that challenge broad theories of Earth system science on boundaries and connectivity emerged to guide study designs to further interrogating disease surveillance and health early warning systems. This is because, climate change (a) drives prevailing biological health hazards as part of forces within the Earth system, (b) shifts disease control services of ecosystems and functioning to effectively regulate disease incidence, and (c) modifies pathogen—species hosts relationships. Hence, the need to rethink pluralistic concepts of climate‐sensitive diseases in their infection and management from a GeoHealth perspective, which "One Health" potentially conveys, and to also maintain ecosystem health. Plain Language Summary: Climate change, an Earth system process (forces deep inside the earth that bring about adjustments) is considered the greatest threat to human health in the twenty‐first century. Climate change has been linked to shifts in ecosystem processes, services and interactions that affect how many organisms thrive including those causing diseases. Yet, how climate change influences specific processes between air, land, life, and water on earth to ultimately affect diseases is still emerging. Collectively partly defined as GeoHealth, it has become topical amidst the coronavirus disease 2019 (COVID‐19) pandemic. Thus, its origin partly links to changes in land‐atmosphere interactions. Hence, the paper poses questions to better appreciate how climate change influence forces within the Earth system to subsequently impact infectious diseases and management in a holistic approach called "One Health," which GeoHealth is positioned to contribute to. Thus, "One Health" provides pathways to environmentally safeguard and sustain ecosystem health and human well‐being. Key Points: Earth system science of ecosystems and climate change hardly tells mechanisms of disease‐causing organisms, detection, and managementRevisiting "One Health" amidst "coronavirus disease 2019" is opportune pathway for GeoHealth's influence of climate‐sensitive infectious diseasesModern Earth system science of climate context of "One Health" is key to maintaining human well‐being and ecosystem integrity [ABSTRACT FROM AUTHOR]

The year 2021 marked the bicentenary of the birth of James Croll (1821–1890), the self‐educated son of a crofter‐stonemason, whose life was characterised by a dizzying range of occupations and homes, poor health and financial concerns, and yet he became a pioneer of orbital dynamics and ice age climate change with an impressive record of publication. Drawing upon archival information and recently published observations, this paper explores selected aspects of Croll's biography, his scientific connections and controversies, and that area of his life relevant to Quaternary science. He was a 19th century polymath whose multifaceted contributions have been a catalyst for subsequent systems‐based climate science on the grand scale, including the foundations for the seminal work of Milutin Milankovitch on the rhythms of Quaternary environmental change. [ABSTRACT FROM AUTHOR]

An important aspect of rainfall estimation is to accurately capture extreme events. Commercial microwave links (CMLs) can complement weather radar and rain gauge data by estimating path‐averaged rainfall intensities near ground. Our aim with this paper was to investigate attenuation induced complete loss of signal (blackout) in the CML data. This effect can occur during heavy rain events and leads to missing extreme values. We analyzed 3 years of attenuation data from 4,000 CMLs in Germany and compared it to a weather radar derived attenuation climatology covering 20 years. We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for longer CMLs (e.g., >10 km) despite their increased dynamic range. Therefore, both the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Plain Language Summary: Commercial microwave links (CMLs) are used to transmit information between towers of cellphone networks. If there is rainfall along the transmission path, the signal level is attenuated. By comparing the transmitted and received signal levels, the average rainfall intensity along the path can be estimated. If the attenuation is too strong, no signal is received, no information can be transmitted and no rainfall estimate is available. This is unfavorable both for network stability and rainfall estimation. In this study, we investigated the frequency of such blackouts in Germany. How many blackouts per year are observed in a 3 year CML data set covering around 4,000 link paths and how many are expected from 20 years of weather radar data? We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for long CMLs, which was an unexpected finding. While only one percent of the annual rainfall amount is missed during blackouts, the probability that a blackout occurs was very high for high rain rates. Both, the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Key Points: Complete loss of commercial microwave link (CML) signals during heavy rain leads to missing rainfall extremesMagnitude of observed blackouts exceeds climatologically expected valuesUnexpectedly, longer CMLs experience more blackouts [ABSTRACT FROM AUTHOR]

Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30‐year (1988–2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS‐TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30‐year annual and monthly climate normals (ACM–Normals) and 30 years of annual, monthly, and daily climate maps (ACM–YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets. [ABSTRACT FROM AUTHOR]

Soviet science contributed significantly to our understanding of anthropogenic climate change and, as part of this, played a central role in the emerging science underpinning climate modification and geoengineering initiatives. A key focus of discussion was the use of stratospheric aerosols linked to the innovative ideas of Mikhail Budyko and colleagues. This work had its origins in what has been termed the theory of aerosol climatic catastrophe, which gained prominence in the Soviet context during the early 1970s. Following the breakup of the Soviet Union, the ideas of Budyko concerning the use of stratospheric aerosols were advanced by Yuri Izrael and his collaborators. The associated body of work gained traction during the 2000s and engendered a wider debate concerning the efficacy of geoengineering solutions amongst Russia's climate scientists. The legacies of this scientific discussion are also evident in recent high‐level international debates such as those linked to the activities of the IPCC. While significant geopolitical obstacles remain in the way of an international agreement linked to the possible deployment of geoengineering measures, interest continues to grow. The maturity of Russian science in the area of geoengineering and climate modification ensures that it remains an important voice within the broader scientific debate. At the same time, the progressive isolation of Russian science from the international scene due to wider geopolitical events risks deflecting attention away from contemporary popular and political debate in this area and alienating this rich scientific tradition at a critical juncture. This article is categorized under:Climate, History, Society, Culture > Ideas and KnowledgeClimate, History, Society, Culture > Disciplinary PerspectivesClimate, History, Society, Culture > Technological Aspects and Ideas [ABSTRACT FROM AUTHOR]

The Floating Potential Measurement Unit onboard the International Space Station includes a Wide sweeping Langmuir Probe that has been operating in the F‐region of the ionosphere at ∼400 km since 2006. While traditional Langmuir probe estimates include critical plasma parameters like electron density and temperature, we have also extracted the O+ percentage from the total ion constituents. This O+ composition data set from the recent minimum in the Solar Cycle 24 reveals orbits with dropouts in O+ to below 80% of the total background ion density at International Space Station orbital altitudes. The observed O+ percentages during these dropouts are much lower than the values predicted by the International Reference Ionosphere 2016 (IRI2016) empirical model. In this paper, we present the climatology of these O+ dropouts with their dependency on season, local time and geographical location. The results show that the lowered O+ percentages are more significant in the winter hemispheres and are routinely observed for orbits in the pre‐sunrise periods. The patterns in O+ dropouts can be explained in part from the lowering of the O+/H+ transition height during solar minimum along with patterns in neutral wind variation. Plain Language Summary: The plasma environment experienced by the International Space Station (ISS) is of key importance to the space physics community. A suite of instruments on the ISS has been operating since 2006 that consist of a spherical Langmuir probe, called the Wide sweeping Langmuir Probe, which is a sensor that periodically collects information about the density and energy of the ambient electrons. Recently, we presented a technique to also infer the composition of O+ ions relative to the total ion density which is an important parameter in understanding the interaction between the terrestrial atmosphere with the exosphere. Results from the most recent period of solar minimum reveal dropouts in O+ densities compared to predictions from empirical models. We show global climatology plots from 2018 to 2019 and observe that these large dropouts in O+ are more prevalent in the post‐midnight region of the winter hemisphere. Past studies have discussed increased presence of lighter H+ ions at higher orbital altitudes, but the fact that O+ dropouts are observed even at ISS altitudes is striking and one of the prominent features of solar minimum. Key Points: O+ percentages often drop below 80% at International Space Station altitudes during solar minimumThe dropouts in O+ abundance are common in post‐midnight sector in winter hemisphereThe climatology of the dropouts is governed by neutral wind variations and lowered O+/H+ transition height [ABSTRACT FROM AUTHOR]

The Dynamic Integrated Climate‐Economy (DICE) model is one of the most influential Integrated Assessment Models available. Its founder Professor William Nordhaus was recently awarded Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel due to his pioneering work on the economics of climate change. In a recent paper in American Economic Journal: Economic Policy, Nordhaus uses the model to conclude that a 2.5°C target is almost out of reach. In this paper, we update DICE 2016 R2 with state‐of‐the‐art models of the carbon cycle, heat uptake into the oceans, and the role of non‐CO2 forcers. We find that the allowable remaining carbon budget (over the period 2015–2100) to meet a 2.5°C target to be 2,360 GtCO2 whereas the estimate obtained using DICE 2016 R2 is about 460 GtCO2. Nordhaus's estimate of the remaining carbon budget for this target is hence five times lower than estimates made by our updated DICE. We also compare our results with estimates by the Intergovernmental Panel on Climate Change (IPCC), and find our results to be in line with the carbon budgets presented in IPCC SR 1.5. We explain the reasons behind the difference between our result and that of Nordhaus and propose that an updated climate module in DICE is warranted. Plain Language Summary: The Dynamic Integrated Climate‐Economy (DICE) model is one of the most commonly used Integrated Assessment Models to analyse climate policy. It was developed by William Nordhaus who subsequently received the Nobel Prize in economics largely as a result of his work with DICE. Nordhaus has used the DICE model to conclude that ambitious climate target appears infeasible as a consequence of inertia in the climate system. In this paper, we show that there are significant problems with the geophysical modules in DICE. This implies that DICE estimates the carbon budget for ambitious climate targets to be many times smaller than the carbon budget estimated by the IPCC. We update DICE with state of the art climate modules and find results in line with IPCC. Through this procedure we can explain why DICE significanty underestimates the carbon budgets. Since DICE has such a strong standing among modellers, economists, and policy makers, understanding that DICE yields a too low carbon budget for ambitious climate targets and why that is the case is important. We also believe that updating the climate science modules of DICE is warranted if it is to be able to capture climate policy aspects relevant for the two degree target. Key Points: We use the DICE model to estimate the available carbon budget for meeting Paris‐styled temperature targetsWe find that the available carbon budget is a factor of five lower than estimates by more advanced climate modelsWe then update DICE using state‐of‐the‐art models of earth system and explain why DICE estimates find a too low carbon budget [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]

The rise in the Earth's global average surface temperature and the increase of extreme weather events have been the focus of scientific discussion in the last decades. Extreme heat combined with other environmental extremes, like high concentrations of air pollution, may induce health problems—especially in socioeconomically vulnerable populations. Spring in South America requires particular attention due to its association with hot, dry weather and air pollution in most parts of the continent. This paper intends to better comprehend the behaviour of heatwaves during the austral winter–spring transition. We propose identifying the spatial coverage, frequency and intensity of heatwaves in homogeneous areas of maximum air temperature near the surface (Tmax). We employed cluster analyses during the period between July and October for 41 years (1979–2019) through the ERA5 reanalysis. Homogeneous Tmax areas in South America were defined by cluster analyses that indicated three homogeneous Tmax regions, as follows: a larger area of the continent including the tropical region (Area 1), eastern and southeastern South America (Area 2) and southernmost and steep areas in the Andes (Area 3). The heatwave events identified via ERA5 reanalysis were classified according to their intensity (intense, moderate and weak events). Spatial frequency and trend analyses were also performed regarding the intensity and persistence of heatwave episodes. These methods allowed the identification of the behavioural aspect of heatwaves spanning the last four decades. The Mann–Kendall statistical test (MK) was applied in order to analyse the heatwave trend with a statistical significance level of 5%. A total of 191 heatwave episodes were identified. Of this total, 47.12, 35.60 and 17.28% of episodes occurred in Areas 2, 3 and 1, respectively. The hotter area extending from northeast to southwest in central South America stood out by its largest frequencies of intense heatwave episodes. Across the continent there was a significant increase in the intensity and persistence of heatwaves over the period of 1979 through to 2019. [ABSTRACT FROM AUTHOR]

In climate science and applications, the term "metric" is used to describe the distillation of complex, multifaceted evaluations to summarize the overall quality of a model simulation, or other data product, and/or as a means to quantify some response to climate change. Metrics provide insights into the fidelity of processes and outcomes from climate models and can assist with both differentiating models' representation of variables or processes and informing whether models are "fit for purpose." Metrics can also provide a valuable reference point for co‐production of knowledge between climate scientists and climate impact practitioners. Although continued metric developments enable model developers to better understand the impacts of decisions made in the model design process, metrics also have implications for the characterization of uncertainty and facilitating analyses of underlying physical processes. As a result, comprehensive evaluation with multiple metrics enhances usability of climate information by both scientific and stakeholder communities. This paper presents examples of insights gained from the development and appropriate use of metrics, and provides examples of how metrics can be used to engage with stakeholders and inform decision‐making. This article is categorized under:Climate Models and Modeling > Knowledge Generation with ModelsThe Social Status of Climate Change Knowledge > Climate Science and Decision MakingAssessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change [ABSTRACT FROM AUTHOR]

Abstract: The ecosystem services (ES) approach is a framework for describing the benefits of nature to human well‐being, and this has become a popular instrument for assessment and evaluation of ecosystems and their functions. Grazing lands can provide a wide array of ES that depend on their management practices and intensity. This article reviews the trends and approaches used in the analysis of some relevant ES provided by grazing systems, in line with the framework principles of the Millennium Ecosystem Assessment (MA). The scientific literature provides reports of many studies on ES in general, but the search here focused on grazing systems, which returned only sixty‐two papers. This review of published papers highlights that: (i) in some papers, the concept of ES as defined by the MA is misunderstood (e.g., lack of anthropocentric vision); (ii) 34% of the papers dealt only with one ES, which neglects the need for the multisectoral approach suggested by the MA; (iii) few papers included stakeholder involvement to improve local decision‐making processes; (iv) cultural ES have been poorly studied despite being considered the most relevant for local and general stakeholders; and (v) stakeholder awareness of well‐being as provided by ES in grazing systems can foster both agri‐environmental schemes and the willingness to pay for these services. [ABSTRACT FROM AUTHOR]

This paper analyses over 10,000 quality‐controlled pilot reports of low‐level wind shear ('wind shear') at the Hong Kong International Airport (HKIA; ICAO code: VHHH) between 2001 and 2019. HKIA is well known for its susceptibility to wind shear, which is a potential hazard for aircraft during landing and take‐off. Wind shear at HKIA exhibits strong seasonality with double peaks in the middle of the spring and summer months. There is a strong diurnal cycle in report numbers, peaking towards the afternoon, as modulated by air traffic at HKIA. On average, there are 115 days per year with wind shear reported, with possible power law distribution in the number of days with different number of reports. By comparing the background wind distribution during those minutes with pilot reports against the climatological distribution, wind shear is observed to be, on average, favoured under higher wind speeds (≥6 m s−1) and reduced under lower speeds with shifted frequency peak and heavier tail, although statistical behaviour can differ significantly across individual runway corridors. Preferred background wind directions for wind shear occurrence highlight the role of terrain influence upstream of HKIA. While positive shear (65.6%) is more often reported than negative shear (34.4%), both types of wind shear events appear to follow an exponential distribution in shear magnitude, with similar rates of decay. This is by far the largest statistical study of low‐level wind shear at an airport. Results can serve as an important local reference in addition to being a useful example for airports around the world. [ABSTRACT FROM AUTHOR]

The planetary boundary layer is crucial for the turbulence mixing and exchange of heat flux, momentum, and atmospheric pollutants between the atmosphere and Earth's surface. Nevertheless, the estimated boundary layer height (BLH) varies greatly by data sources and algorithms. This paper seeks to characterize the convective BLHs from 1‐s radiosonde measurements of China Radiosonde Network (CRN) for the 2012–2020 period at 1400 Beijing time, by using eight well‐known algorithms, wherein the newly established Thorpe method is theoretically fundamental on turbulence analysis and is in remarkable agreement with the parcel and bulk Richardson number methods. BLHs obtained by eight methods, ranging from 1.2 to 2.5 km, strongly vary with methods based on different kinetic or thermodynamic theories. The significant offsets among methods motivate the present study to propose a merged algorithm, to minimize the spread of BLH estimate. The merged BLH contains more physical information than that retrieved from a single method and can significantly lower the uncertainty. The BLH climatology exhibits a spatial pattern of "Southwest‐High Southeast‐Low," ascending from 0.6 km over southeastern China to 2.3 km over southwestern China, which could be largely attributed to the variations in the integrated surface sensible heat flux, soil moisture, total cloud cover, land cover, synoptic forcing, and terrain‐induced flow. Of interest is that land surface properties could be the major driver for the development of CBL. Also noteworthy is that the smallest BLH is expected under the strongest synoptic forcing condition. Plain Language Summary: The planetary boundary layer (PBL), where the exchanges of heat, moisture, momentum, and mass mainly occur with a variety of physical and chemical processes involved, is the lowest part of the troposphere in direct contact with the ground surface. However, the determination of boundary layer height (BLH) tends to be dramatically influenced by the dataset and methods used. Few studies have been conducted to evaluate the discrepancy of nationwide daytime BLH over China originating from the methods used. In particular, eight well‐known methods, including the novel Thorpe method and seven other traditional methods, are applied to estimate BLHs from high‐resolution soundings from the China Radiosonde Network. However, BLH derived by eight methods vary from approximately 1.2 to 2.5 km, strongly depending on the method. Therefore, to compile a more convincing BLH dataset eight methods are selected and composed as a merged BLH. The climatology of the merged BLH for the period 2012–2020 shows a well‐defined "Southwest‐High Southeast‐Low " pattern, which could be attributed to the diversities in sensible heat flux, soil moisture, land cover, total cloud cover, synoptic forcing, and terrain‐induced flow across China. Key Points: The merged boundary layer height (BLH) is synthesized from eight methods and it carries more planetary boundary layer (PBL) information and can considerably lower the uncertaintiesThe "Southwest‐High Southeast‐Low" pattern of BLH could be attributed to land properties, synoptic forcing, and terrain‐induced flowUnder weak synoptic forcing condition the development of convective PBL is more susceptible to the variation of land surface properties [ABSTRACT FROM AUTHOR]

Sinking atolls are an enduring symbol of the power of climate change to destroy inhabited places. Climate impact science and the media share a panoptic gaze on atoll islands seeing them as being small, inert and passive in the face of rising seas. The focus in these accounts is on the power of water as the agent of destruction, while the agency of the assemblage of human and non‐human actors that is the (is)land itself is ignored. Thus, atolls are said to be vulnerable, and the prevailing ideas of adaptation are either international relocation to avoid the sea or seawalls to contain it. Based on qualitative field research in Pacific atolls, this paper examines the connections between island peoples and their terrestrial environments, and the work that they are doing in response to the impacts of climate change. It shows how land is conceived symbolically, socio‐culturally and legally, and considers its role in sustaining livelihoods and anchoring identities through a changing climate. [ABSTRACT FROM AUTHOR]