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This systematic meta-narrative literature review aims to explore the narratives of trust evident in literature on public (mis)trust relating to climate science published up until May 2021, and to present the main findings from these papers. We identified six narratives of trust: attitudinal trust, cognitive trust, affective trust, contingencies of trust, contextual trust and communicated trust. The papers' main findings spanned theoretical conclusions on the importance of positionality to trust and morality to trustworthiness, to qualitative findings that the scientific community was mainly trusted, to quantitative findings that explored how trust functioned as an independent, dependent or mediating variable. This literature review sheds important light on the interrelationship between climate science and publics, highlights areas for further research, and in its characterisation of trust narratives provides a language for conceptualising trust that can further interdisciplinary engagement. [ABSTRACT FROM AUTHOR]

Understanding carbon sources and sinks across the Earth's surface is fundamental in climate science and policy; thus, these topics have been extensively studied but have yet to be fully resolved and are associated with massive debate regarding the sign and magnitude of the carbon budget from global to regional scales. Developing new models and estimates based on state-of-the-art algorithms and data constraints can provide valuable knowledge and contribute to a final ensemble model in which various optimal carbon budget estimates are integrated, such as the annual global carbon budget paper. Here, we develop a new atmospheric inversion system based on the 4D local ensemble transform Kalman filter (4D-LETKF) coupled with the GEOS-Chem global transport model to infer surface-to-atmosphere net carbon fluxes from Orbiting Carbon Observatory-2 (OCO-2) V10r XCO 2 retrievals. The 4D-LETKF algorithm is adapted to an OCO-2-based global carbon inversion system for the first time in this work. On average, the mean annual terrestrial and oceanic fluxes between 2015 and 2020 are estimated as - 2.02 and - 2.34 GtC yr -1 , respectively, compensating for 21 % and 24 %, respectively, of global fossil carbon dioxide (CO 2) emissions (9.80 GtC yr -1). Our inversion results agree with the CO 2 atmospheric growth rates reported by the National Oceanic and Atmospheric Administration (NOAA) and reduce the modeled CO 2 concentration biases relative to the prior fluxes against surface and aircraft measurements. Our inversion-based carbon fluxes are broadly consistent with those provided by other global atmospheric inversion models, although discrepancies still occur in the land–ocean flux partitioning schemes and seasonal flux amplitudes over boreal and tropical regions, possibly due to the sparse observational constraints of the OCO-2 satellite and the divergent prior fluxes used in different inversion models. Four sensitivity experiments are performed herein to vary the prior fluxes and uncertainties in our inversion system, suggesting that regions that lack OCO-2 coverage are sensitive to the priors, especially over the tropics and high latitudes. In the further development of our inversion system, we will optimize the data-assimilation configuration to fully utilize current observations and increase the spatial and seasonal representativeness of the prior fluxes over regions that lack observations. [ABSTRACT FROM AUTHOR]

Aerosol–cloud interactions play a crucial role in shaping Earth's climate and hydrological cycle. Observing these interactions with high precision and accuracy is of the utmost importance for improving climate models and predicting Earth's climate. Over the past few decades, lidar techniques have emerged as powerful tools for investigating aerosol–cloud interactions due to their ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and temporal resolutions. This review paper provides an overview of recent advancements in the study of ACI using lidar techniques. The paper begins with a description of the different cloud microphysical processes that are affected by the presence of aerosol, and with an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The subsequent sections delve into the key findings and insights gained from lidar-based studies of aerosol–cloud interactions. This includes investigations into the role of aerosol particles in cloud formation, evolution, and microphysical properties. Finally, the review concludes with an outlook on future research. By reporting the latest findings and methodologies, this review aims to provide valuable insights for researchers engaged in climate science and atmospheric research. [ABSTRACT FROM AUTHOR]

A 30-year (1980–2010) climatology of the major variables and terms of the transformed Eulerian-mean (TEM) momentum and thermodynamic equations is constructed by using four global atmospheric reanalyses: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); the Japanese 55-year Reanalysis (JRA-55); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Climate Forecast System Reanalysis (CFSR). Both the reanalysis ensemble mean (REM) and the differences in each reanalysis from the REM are investigated in the latitude–pressure domain for December–January–February and for June–July–August. For the REM investigation, two residual vertical velocities (the original one and one evaluated from residual meridional velocity) and two mass streamfunctions (from meridional and vertical velocities) are compared. Longwave (LW) radiative heating and shortwave (SW) radiative heating are also shown and discussed. For the TEM equations, the residual terms are also calculated and investigated for their potential usefulness, as the residual term for the momentum equation should include the effects of parameterized processes such as gravity waves, while that for the thermodynamic equation should indicate the analysis increment. Inter-reanalysis differences are investigated for the mass streamfunction, LW and SW heating, the two major terms of the TEM momentum equation (the Coriolis term and the Eliassen–Palm flux divergence term), and the two major terms of the TEM thermodynamic equation (the vertical temperature advection term and the total diabatic heating term). The spread among reanalysis TEM momentum balance terms is around 10 % in Northern Hemisphere winter and up to 50 % in Southern Hemisphere winter. The largest uncertainties in the thermodynamic equation (about 50 %) are found in the vertical advection, for which the structure is inconsistent with the differences in heating. The results shown in this paper provide basic information on the degree of agreement among recent reanalyses in the stratosphere and upper troposphere in the TEM framework. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) can cause severe wind and rain hazards. Unusual TC tracks and their extreme precipitation forecasts have become two difficult problems faced by conventional models of primitive equations. The case study in this paper finds that the numerical computation of the climatological component in conventional models restricts the prediction of unusual TC tracks. The climatological component should be a forcing quantity, not a predictor in the numerical integration of all models. Anomaly-based variable models can overcome the bottleneck of forecast time length or the one-week forecasting barrier, which is limited to less than one week for conventional models. The challenge in extreme precipitation forecasting is how to physically get the vertical velocity. The anomalous moisture stress modulus (AMSM), as an indicator of heavy rainfall presented in this paper, considers the two conditions associated with vertical velocity and anomalous specific humidity in the lower troposphere. Vertical velocity is produced by the orthogonal collision of horizontal anomalous airflows. [ABSTRACT FROM AUTHOR]

In recent years, various early warning signals of critical transition have been presented, such as autocorrelation at lag 1 [AR(1)], variance, the propagator based on detrended fluctuation analysis (DFA-propagator), and so on. Many studies have shown that the climate system has the characteristics of long-term memory (LTM). Will the LTM characteristics of the climate system change as it approaches possible critical transition points? In view of this, the present paper first studies whether the LTM of several folding (folded bifurcation) models changes consistently as they approach their critical points slowly by the rescaled range (R/S) analysis. The results of numerical experiments show that when the control parameters of the folding model are close to its critical threshold, the Hurst exponent H exhibits an almost monotonic increase (significance level α = 0.05). We compare the performance of R/S with the existing indicators, including AR(1), variance, and DFA-propagator, and find that R/S is a perfectly valid alternative. When there is no extra false noise, AR(1) and variance have good early warning effects. After the addition of extra Gaussian white noise of different intensities, the values of AR(1) and variance change significantly. As a result, the DFA-propagator based on AR(1) calibration also changed significantly. Compared with the other three indicators, the early warning effect of H has stronger ability to resist the interference of external false signals. To further verify the validity of increasing H, paleoclimate reconstruction of Cariaco Basin sediment core grayscale record with long trends filtered out is studied by R/S analysis. The other three early warning signals are calculated in the same way. The data contain a well-known abrupt climate change: the transition between the Younger Dryas (YD) and the Holocene. We find that approximately 300 years before this abrupt climate change occurred, before 11.7 kyr BP, the LTM exponents for Cariaco Basin deglacial grayscale data present an obvious increasing trend at a significant level of α = 0.05. Meanwhile, the variation trend of H and DFA-propagator is basically similar. This shows that increasing H by R/S analysis is an effective early warning signal, which indicates that a dynamic system is approaching its possible critical transition points; H is a completely valid alternative signal for AR(1) and DFA-propagator. The main conclusion of this paper is based on numerical experiments. The precise relationship between H and the stability of the underlying state approaching the transition needs to be further studied. [ABSTRACT FROM AUTHOR]

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]

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model's precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

In the online seminar series "Perspectives on climate sciences: from historical developments to future frontiers", which took place during 2020–2021, well-known and established scientists from several fields – including mathematics, physics, climate science and ecology – presented their perspectives on the evolution of climate science and on relevant scientific concepts. This special issue aims to create a platform for a more detailed elaboration of the topics discussed in the seminars but also to publish new scientific findings. In this paper, we first give an overview of the content of the seminar series, and then we introduce the written contributions to this special issue. In line with the spirit of the seminar series, this paper is structured along thematic areas of the broad field of climate science, conveying different perspectives on the climate system: geophysical fluid dynamics, dynamical systems theory, multiscale processes, statistical physics, paleoclimate and the human dimension. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a spectral surface reflectivity climatology based on observations made by TROPOMI on board the Sentinel-5P satellite. The database contains the directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface for 21 wavelength bands ranging from 328 to 2314 nm and for each calendar month. The spatial resolution of the database grid is 0.125° × 0.125°. A recently developed cloud shadow detection technique is implemented to avoid dark scenes due to cloud shadow. In the database, the anisotropy of the surface reflection is described using a third-order parameterisation of the viewing angle dependence. The viewing angle dependence of the DLER is analysed globally and for a selection of surface type regions. The dependence is found to agree with the viewing angle dependence found in the GOME-2 surface DLER database. Differences exist, related to the actual solar position. On average, the viewing angle dependence in TROPOMI DLER is weaker than for GOME-2 DLER, but still important. Validation of the new database was first performed by comparison of the non-directional TROPOMI surface LER with heritage LER databases based on GOME-1 , OMI, SCIAMACHY, and GOME-2 data. Agreement was found within 0.002–0.02 in the UV-VIS (below 500 nm), up to 0.003 in the NIR (670–772 nm), and below 0.001 in the short-wave infrared (SWIR) (2314 nm). These performance numbers are dominated by the performance over ocean, but they are in most cases also representative for land surfaces. For the validation of the directional TROPOMI surface DLER, we made use of comparison with the MODIS surface bi-directional reflectance distribution function (BRDF) for a selection of surface type regions. In all cases the DLER performed significantly better than the traditional LER, and we found good agreement with the MODIS surface BRDF. The TROPOMI surface DLER database is a clear improvement on previous surface albedo databases and can be used as input not only for satellite retrievals from TROPOMI observations, but also for retrievals from observations from other polar-orbiting satellite instruments provided that their equator crossing time is close to that of TROPOMI. The algorithm that is introduced in this paper can be used for the retrieval of surface reflectivity climatologies from other polar satellite missions as well, including Ocean and Land Colour Instrument (OLCI) on the Sentinel-3 satellites, Sentinel-5, and Multi-viewing Multi-channel Multi-polarisation imager (3MI) on the MetOp-SG-A1 satellite to be launched in 2025, as well as the future CO2M mission. [ABSTRACT FROM AUTHOR]

The concept of a digital twin is intriguing as it presents an innovative approach to solving numerous real-world challenges. Initially emerging from the domains of manufacturing and engineering, digital twin research has transcended its origins and now finds applications across a wide range of disciplines. This multidisciplinary expansion has impressively demonstrated the potential of digital twin research. While the simulation aspect of a digital twin is often emphasized, the role of artificial intelligence (AI) and machine learning (ML) is severely understudied. For this reason, in this paper, we highlight the pivotal role of AI and ML for digital twin research. By recognizing that a digital twin is a component of a broader Digital Twin System (DTS), we can fully grasp the diverse applications of AI and ML. In this paper, we explore six AI techniques—(1) optimization (model creation), (2) optimization (model updating), (3) generative modeling, (4) data analytics, (5) predictive analytics and (6) decision making—and their potential to advance applications in health, climate science, and sustainability. [ABSTRACT FROM AUTHOR]

Article 7 of the Paris Agreement recognizes that adaptation is a 'global challenge faced by all with local, regional and international dimensions.' It further establishes the 'global goal on adaptation focusing on enhancing adaptive capacity, strengthening resilience and reducing vulnerability to climate change, with a view to contributing to sustainable development.' However, the lack of international cooperation between the global north and global south challenge the formulation and implementation of climate change adaptation strategies. This paper brings in the concept of global public goods (GPGs) to the lexicon of climate adaptation and highlights that adverse impacts of climate change such as climate-induced global migration are global public bad. Hence, the measures taken to respond to such impacts, which consequently enhance the resilience of affected countries, make them more adaptive to those adverse impacts, and deliver common values of universal character, should be construed as the global public good. The paper argues that that the idea of GPGs with its universality offers a normative and practical foundation for understanding, addressing, and strengthening the international community's climate adaptation actions and cooperation. [ABSTRACT FROM AUTHOR]

Promoting climate change discourses may potentially create a useful space for addressing the effects of climate change in Tanzania. During disasters such as floods, everyone obfuscates their agency and blames the other for causing climate change effects. This paper examines the discourse of climate change among scientists, government officials, and the non-scientific Tanzanian stakeholders on climate change and adaptation strategies after the 2019-2020 effects. These actors obfuscate responsibilities and attribute blame on the other for accelerating climate change effects. This paper examines how these attributions contribute to enhancing resilience and adaptation strategies. The paper was theoretically guided by the divided subject and positioning theories. Data for this paper were gathered from YouTube covering the subject. The results suggest that while none of the actors accepts responsibility for accelerating climate change; "what is happening and what should be done" gradually turn into "who is responsible for the mess". Divisive discourse like this is likely to keep Tanzania wobbling to create a resilient society to confront the challenges of climate change. The paper raises the need for the facilitation of a better communication between climate change science and the non-scientific audience. [ABSTRACT FROM AUTHOR]

B I Sustainability and Climate Change i b is a peer-reviewed publication dedicated to furthering the science of sustainability, sustainable development, and climate change. The editorial team of B I Sustainability and Climate Change i b invites you to submit your manuscript for consideration. [Extracted from the article]

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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]

Heritage science is an inherently practice-oriented field that aims to support our understanding, and conservation, of heritage. Research is commonly undertaken using laboratory or field-based methodologies, but given the ethical and scale constraints, over time and space, of these approaches, process-based models should provide a tool for exploring practical solutions. Unlike other fields, such as climate science and ecology, there appears limited engagement with modelling within heritage science. The characteristics and use of processed-based models published in the field is examined to explore tensions in using models to transfer understanding between research and practice. By examining models that investigate interactions between heritage materials and environment, we find that, at best, model outputs may be used by other researchers or occasionally by heritage institutions; or more commonly, the model's existence is used as a justification of research, yet without meaningful engagement within either the academic and heritage practitioner communities. Some models are unlikely to be used in practice as they have been developed at spatial or temporal scales incompatible with being truly applicable to objects or sites, or can seem to advance theory without engaging with practice. The uptake of models by researchers who rerun or change the code is rare. Models that seem to gain substantial use appear to benefit from graphical user interfaces that make them easy to run. Evidence of models in solving real-world conservation problems is hard to find. This may arise because practical applications are rarely reported in academic journal literature and open access publications. There is some evidence they are revealed in conferences and possibly internal heritage organisation reports, but this gray literature doesn't readily feedback into the development and refinement of existing models. It is likely the use of models would increase if mechanisms were available to support the development of user interfaces, training workshops and the ability of practical use cases to be fed back to the modelling community. [ABSTRACT FROM AUTHOR]

In the North West Shelf region of Australia is a surface current (Holloway Current), which flows southwestward along the shelf break. This paper describes a seasonal undercurrent below the Holloway Current. A 5-day climatology is constructed from the output of an eddy-resolving oceanic general circulation model (OGCM). A seasonal northeastward-flowing undercurrent is found on the upper continental slope during the climatological April-May. This undercurrent reverses during February-March. During its annual cycle, the phase of the undercurrent tends to propagate southwestward and upward. The annual frequency dominates, but the positive and negative phases of the undercurrent are not symmetric in the yearly cycle because of the contributions from the semi-annual and 1/3-annual components. We propose a hypothesis that this undercurrent is a beam of coastal trapped wave (CTW). As an initial attempt to assess the plausibility of this hypothesis, we construct an idealized linear coastal-trapped wave (CTW) solution driven by an idealized harmonic meridional winds at the annual frequency. The solution takes the form of a beam originating from the forcing region on the continental shelf and propagating offshore and southward. When it emerges on to the continental slope, it takes the form of an undercurrent. This idealized solution shares several properties with the undercurrent in the OGCM despite several discrepancies. [ABSTRACT FROM AUTHOR]

The optimal control problems of degenerate parabolic equations have many applications in economics, physics, climatology, and so on. Motivated by the applications, we consider the optimal control problems of a class of nonlinear degenerate parabolic equations in this paper. The main result is that we deduce the first order necessary condition for the optimal control problem of nonlinear degenerate parabolic equations by variation method. Moreover, we investigate the uniqueness of the solutions to the optimal control problems. For the linear equations, we obtain the global uniqueness, while for the nonlinear equations, we obtain only the local uniqueness. Finally, we give a numerical example to validate the theoretical results. [ABSTRACT FROM AUTHOR]

Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement: The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities. [ABSTRACT FROM AUTHOR]

The Global Historical Climatology Network (GHCN) monthly precipitation dataset contains historical time series for thousands of land surface stations worldwide. Initially released in 1992 and revised in 1998, the dataset has been employed in a variety of applications over the past three decades, including operational monitoring, applied research, and international assessments. This paper describes the data and methods used to compile the latest edition (version 4), which has three major enhancements. The first enhancement is to the station network, which increased in size by a factor of five due to the inclusion of dozens of new source datasets, most notably GHCN Daily (GHCNd). The second improvement is the application of a rule-based algorithm to compare and merge records representing the same location. The third enhancement is to the quality assurance approach, now consisting of 18 new checks based on GHCNd and other operational systems. Updated monthly, the resulting dataset consists of time series of monthly precipitation totals at more than 120,000 worldwide stations, including more than 33,000 active observing sites. [ABSTRACT FROM AUTHOR]

Education needs to be at the forefront of the efforts to mitigate and adapt to Climate Change (CC) effects. We have introduced the Climate Change Competence (C3) to provide a comprehensive route to include the topics of sustainability and CC into the curriculum. This paper analyses how different primary and secondary teachers can improve this competence using a Massive Online Open Course (MOOC) on the basic science of CC. While the improvement in knowledge is expected, we have observed important advances on abilities, and attitudes on CC which are also essential to teach about CC mitigation and adaptation. The present study used a pre-experimental design, with pre-and post-test measurements with a sample of 530 students. While the MOOC focuses on the Science of CC, the participants also advance in attitudes and abilities, revealing an important correlation between these three dimensions of competence. Also, the study reveals that the MOOC improves the C3 in all the participants, but it does in diverse ways for diverse groups. [ABSTRACT FROM AUTHOR]

Various analyses show that right-wing populist parties (RWP) tend to be sceptical of climate science and policy. This points to a blank space in the dominant analyses of populism: their blindness towards society-nature relations. This paper aims to develop an approach grounded in Cultural Political Economy (CPE) that can be used to decipher the mediation of RWP within the context of economic, political, and cultural developments as well as society–nature relations. Against this background, the argument is developed that RWP is concerned not only with countering migration and processes of societal liberalisation, but also with defending an existing way of life that is firmly rooted in the destructive appropriation of nature. As a current of right-wing politics, RWP defends the imperial mode of living by expressing scepticism towards the existence of anthropogenic climate change. The paper contributes to a better understanding of the political economy of RWP by linking the dimensions of social domination with the appropriation of nature. [ABSTRACT FROM AUTHOR]

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]

Based on the best-track dataset from the Shanghai Typhoon Institute/China Meteorological Administration, the paper provides a comprehensive summary and analysis of tropical cyclone (TC) activities in the Western North Pacific (WNP) and the South China Sea (SCS) for 2022. Using the historical climatology from 1951 to 2020, the anomalous conditions during 2022 in TC frequency, origin locations, tracks, intensity, and duration for the entire ocean basin as well as landfall events in China are examined. Results show that the overall TC frequency is slightly lower than normal, but the multiple TC events have a very high frequency of occurrence. Origin locations of TCs, which mark the starting points of their paths, show a large westward and northward deviation from climatology. Around 40% of the named TCs exhibit a shift in their direction of movement from westerly to easterly. Additionally, comparisons of the means, medians, upper and lower quartiles all indicate that the intensity of TCs in 2022 is generally lower than the climatology, with the duration of TCs at tropical storm intensity or above being shorter than usual. A notable observation is the fewer incidence of TC landfalls in China, but with a geographical concentration in Guangdong Province. These anomalous annual TC activities are influenced by related atmospheric and oceanic environmental conditions modulated by multi-scale climate variability. The findings provide useful information for enhancing disaster mitigation strategies in the Asia-Pacific region. [ABSTRACT FROM AUTHOR]

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales. [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]

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]

While our knowledge of tropical montane forests (TMFs) is limited, human activities continue to damage them. Remote sensing has shown its usefulness for forest investigations with difficult terrain. This study systematically reviewed the applications of spaceborne remote sensing to TMFs with regards to time, spatial distribution, journal publication, methodology, theme, sensor systems, vegetation zones, research needs, and techniques applied. Our review found that the number of papers published between 1997 to 2021 has increased considerably. Experts used optical sensors with low to medium spatial resolution (85.76%), and that the use of synthetic aperture radar received little attention (12.70%). Published articles varied substantially between continents, with the Americas having the most studies (62.26%), leaving Asia, Africa, and Oceania behind. Most research has focused on forestry (42.66%), climate science (11.01%), and disaster management (9.63%). This paper highlighted the challenges and opportunities in the application of remote sensing in TMF conservation and management. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to discuss the concept of information in relation to temporality within the context of climate change communication. Furthermore, the paper aims to highlight the empirical richness of information as a concept by analysing its use in context. Design/methodology/approach: The discussion is based on 14 semi-structured interviews with initiators and collaborators of 6 open letters on climate change published in 2018–2019. By taking three specific notions the interviewees introduced—fast food information, information quality and information gap–as the analytical point of departure, the study aims for a contextual understanding of information grounded in temporal sensitivity. Findings: The paper finds that information in the context of open letters is informed by different, and at times contradicting, temporalities and timescapes which align with various material, institutional and discursive practices. Based on this finding, the paper argues that notions of information are intrinsically linked to the act of communicating, and they should be viewed as co-constituting each other. Originality/value: The paper contributes with an empirically informed discussion regarding the concept of information as it is used in a specific context. It illustrates how "information" is far from being understood in a singular fashion, but is made up of multifaceted and at times contradictory understandings. Ultimately, they correspond to why and how one communicates climate change information. [ABSTRACT FROM AUTHOR]

This paper illustrates the potential impacts of climate change on financial markets, focusing on their long-term significance. It uses a top-down modelling tool developed by Ortec Finance in partnership with Cambridge Econometrics that combines climate science with macro-economic and financial effects to examine the possible impacts of three plausible (not extreme) climate pathways. The paper first considers the impact on gross domestic product (GDP), finding that GDP is lower in all three pathways, with the most severe reduction in the Failed Transition Pathway where the Paris Agreement climate targets are not met. The model then translates these GDP impacts into financial market effects. In the Failed Transition Pathway, cumulative global equity returns are approximately 50% lower over the period 2020–2060 than in the climate-uninformed base case. For the other two pathways where the Paris Agreement targets are met, the corresponding figures are 15% and 25% lower returns than in the base case. Results are provided for other asset classes too. These demonstrate that climate change represents a significant market risk, with implications for financial planning, modelling and regulation. [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%. Significance Statement: Numerical weather prediction (NWP) is the "go-to" approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP. [ABSTRACT FROM AUTHOR]

This paper explains the importance of environmental care for the growth and development of the global world and how One Belt One Road Initiative (OBOR) can help to connect Asia, Europe, and Africa for economic cooperation and long-term sustainability. Paper, based on bibliometric analysis and utilizing Web of Science Core Collection database for calculation, summarizes total OBOR publications in terms of quantity, citations, regions, disciplines, institutes and explains about the most productive countries. Results reveal that total OBOR papers indexed in Web of Science was found 2508 where there were 1335 articles and 854 proceedings papers marking 87.3% of total OBOR research. We find the most productive direction of environmental science and global climate change and work related to environmental policies. Results show that China has contributed 73.3% (1755 publications) to global OBOR publication and have received 69.3% of all citations to "One Belt One Road" publications. Highest average citation level was from Canada, Portugal, and Spain while China's average citations were quite low though very close to average world level. Internationally collaborated publications had more citations than the publications prepared by authors of one country. The paper concludes that environmental concerns should be kept in mind while focusing on the growth and development. [ABSTRACT FROM AUTHOR]

This paper presents climatological features of the longitudinal structures WN4, WN3, and WN2 and their drivers observed in the lower thermospheric temperatures and in the ionospheric TEC. For this purpose, two long-term data sets are utilized: the satellite SABER/TIMED temperature measurements, and the global TEC maps generated with the NASA JPL for the interval of 2002–2022. As the main drivers of the longitudinal structures are mainly nonmigrating tides, this study first investigates the climatology of those nonmigrating tides, which are the main contributors of the considered longitudinal structures; these are nonmigrating diurnal DE3, DE2, and DW2, and semidiurnal SW4 and SE2 tides. The climatology of WN4, WN3, and WN2 structures in the lower thermosphere reveals that WN4 is the strongest one with a magnitude of ~20 K observed at 10° S in August, followed by WN2 with ~13.9 K at 10° S in February, and the weakest is WN3 with ~12.4 K observed over the equator in July. In the ionosphere, WN3 is the strongest structure with a magnitude of 5.9 TECU located at −30° modip latitude in October, followed by WN2 with 5.4 TECU at 30 modip in March, and the last is WN4 with 3.7 TECU at −30 modip in August. Both the climatology of the WSA and the features of its drivers are investigated as well. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed at investigating employee training, employee participation and organizational commitment (OC) and the moderating effect of workplace ostracism among bank employees. Design/methodology/approach: The study used a descriptive and cross-sectional design with the aid of a standard scale constructed into a questionnaire. Cluster, convenience and simple random sampling techniques were used to select 1,067 respondents, of which 870 were deemed fit for the study. The theories underpinning the study were the social exchange theory (SET) and social identity theory (SIT). Four hypotheses were developed and tested using hierarchical multiple regression analysis, and moderation using PROCESS macro. Findings: The study found that employee training and employee participation had a significant positive relationship with organizational commitment, while organizational ostracism had a significant but negative relationship with organizational commitment among bank employees. The study also found that workplace ostracism moderated the relationship between organizational climate and organizational commitment The study recommended that organizational commitment requires management training their workforce, allowing employee participation in decisions, and minimizing or outrightly eradicating the practice of organizational ostracism. It is, therefore, concluded that workers place great value on training and participation in decision-making and frown at organizational ostracism. Originality/value: This paper fills in the gaps left by the paucity of empirical investigation of the moderating role that workplace ostracism plays between employee training, employee participation and organizational commitment – a feat that is lacking in developing countries. It serves as a reminder to management to prevent or entirely eliminate workplace ostracism to allay an employee's impression of being a threat to an organization when commitment is low. [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]

Dimension reduction techniques are an essential part of the climate analyst's toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative, sparse, and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection. [ABSTRACT FROM AUTHOR]

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Research Question: The aim of this paper is to determine the factors influencing safety climate in different industrial sectors in project-based organizations. Motivation: Systematic research on occupational safety is not so frequent in the Republic of Serbia. The lack of research that deals with safety climate in project-based organizations exploring the attitudes of employees in different industrial sectors is evident. In the research conducted by Milijic et al. (2013) a model for measuring safety climate factors was proposed and the following step is to identify the most important safety climate issues for each industrial sector as a key to fostering better safety performance. Chen et al. (2018a) concluded that engagement at all levels of the company is essential to fostering better safety performances as well as that safety awareness is the most important factor influencing workers' safety efficiency. The authors of the present paper seek those particular factors that adequately describe safety climate in project-based organizations by examining the attitudes of workers in different industrial sectors. Idea: The main idea of this paper is to evaluate and rank safety climate in different industrial sectors and different workplaces considering the following five factors of safety climate: Safety and competence awareness, Occupational safety management commitment, Safety training and tools, Safety practices and procedures and Organizational environment. Data: Safety climate analysis was conducted on a set of data collected as a part of a survey carried out in eleven project-oriented organizations operating in different industrial sectors on the territory of Serbia. Tools: This paper proposes a multi-criteria analysis of the data collected from a national survey using the Entropy-PROMETHEE-GAIA method. Findings: A complete ranking of safety climate in project-oriented organizations based on the opinions of employees in different industrial sectors resulted in Energy as the bestranked sector. On the other hand, the worst-ranked sector is High-rise construction. The results of the complete ranking of safety climate in project-oriented organizations based on the opinions of employees at different workplaces in different industrial sectors showed that the best-ranked alternatives were manager working places. When it comes to production workers on projects, the most favorable safety climate is in energy workplaces and for non-productive workers, while the most favorable safety climate is on projects in the field of mechanical engineering. Contribution: Adopting the conclusions from the paper can, in practice, improve safety climate along with safety at work in project-based organizations. [ABSTRACT FROM AUTHOR]

The article discusses citizen science and climate services in cities, highlighting current practices, new approaches, and future opportunities for enhancing urban climate resilience. Topics include the role of citizen engagement and co-design in transforming urban climate resilience, the use of advanced interactive technologies and crowdsourcing for community-driven climate monitoring, and the development of co-produced climate services for improved urban decision-making and adaptation.

This paper studies early arguments in Sweden for combating climate change. We show how scientific results in relation to climate change entered the political sphere as part of the debate on energy in the 1970s, a process we propose to name energysation. We argue that the use of climate science by pro-nuclear political actors served as a way of maintaining a course set by a high-energy society while simultaneously trying to outmanoeuvre the growing environmental anti-nuclear and low-energy movement. When the pro-nuclear power side met with resistance, this led to a displacement of climate change knowledge away from the realm of the national political sphere and specific energy forms, a process we conceptualise as de-energysation. By highlighting conflicts and the political framings of climate change in the early years 1974-1983, we suggest that the history of these frames influences current delay in climate change mitigation and limits the range of actions and ways of addressing the ongoing climate emergency. [ABSTRACT FROM AUTHOR]

What does it entail to study the climate crisis from – or consistently with – a critical realist perspective? The paper addresses this question in three steps. First, it considers the boundaries of critical realism in relation to climate crisis research. In this context it identifies climate science as a field that in important respects resonates implicitly with critical realism. Conversely, a book by human ecologist Andreas Malm is introduced as an example of a work that, while sympathetic to critical realism, in key respects contradicts core features of it. Second, to illustrate what an analysis of the crisis informed by critical realism can look like, the paper brings into focus the main causes of the climate crisis – including the capitalist growth imperative, neoliberalism, and consumer culture. Finally, the status quo project, the green growth project and the degrowth project are identified as fundamentally different ways of approaching the climate crisis. [ABSTRACT FROM AUTHOR]

Antarctica is a crucial regulator of the world's climate, and as environmental security permeates global security, using Antarctic science to better understand climate is becoming increasingly pressing. Although the Australian Government has recognised that climate change poses 'a current and existential national security' threat and has acknowledged Antarctica's importance regarding the earth's global climate system, the focus of Australia's intelligence community pertaining to Antarctica currently remains restricted to upholding the military-security and diplomatic goals of the Antarctic Treaty System (ATS). This current focus aims to hedge against the possibility of conflict on, or over, the frozen continent via 'working the ATS', however, this paper argues that Antarctic climate science holds a greater capacity to deliver security outcomes for Australia. Antarctic climate science offers opportunities regarding intelligence for Antarctica, that is, securing Australia's Antarctic interests, as well as regarding intelligence from Antarctica; by enhancing natural disaster preparedness, bolstering broader strategic planning, as well as furthering diplomacy and the legitimisation of Australia's leadership on, and over, the frozen continent. It is recommended that the Commonwealth Government establish a climate intelligence working group to ensure the utility of climate science to security and intelligence is realised. [ABSTRACT FROM AUTHOR]

Copyright of GeoHumanities is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper draws on Deborah Britzman's conceptualisation of 'difficult knowledge' and Michael Rothberg's figure of 'the implicated subject' to advance a Social Ecology of Responsibility Framework (SERF) in relation to the climate crisis.This framework demonstrates the impossibility of disarticulating individual, private actions that contribute to the ecological crisis from state-corporate climate-related harms. While not discounting differences of scale between individual actions and state-corporate crimes, the article highlights difficulties with binaristic approaches to climate responsibility which privilege either personal actions or macro-level norms, practices and ideologies. Foregrounding self-implication, the model serves as a basis for establishing transnational and transgenerational solidarity with human and other-than-human lifeforms who inhabit the Earth. The paper concludes with some examples of visual images and accompanying activities that can be used to prompt critical reflection on one's own positioning as an implicated subject and as a change agent who can contribute to the amelioration of global warming. [ABSTRACT FROM AUTHOR]

Causal discovery from river runoff data aids flood prevention and mitigation strategies, garnering attention in climate and earth science. However, most climate causal discovery methods rely on conditional independence approaches, overlooking the non-stationary characteristics of river runoff data and leading to poor performance. In this paper, we propose a river runoff causal discovery method based on deep reinforcement learning, called RCD-DRL, to effectively learn causal relationships from non-stationary river runoff time series data. The proposed method utilizes an actor-critic framework, which consists of three main modules: an actor module, a critic module, and a reward module. In detail, RCD-DRL first employs the actor module within the encoder-decoder architecture to learn latent features from raw river runoff data, enabling the model to quickly adapt to non-stationary data distributions and generating a causality matrix at different stations. Subsequently, the critic network with two fully connected layers is designed to estimate the value of the current encoded features. Finally, the reward module, based on the Bayesian information criterion (BIC), is used to calculate the reward corresponding to the currently generated causal matrix. Experimental results obtained on both synthetic and real datasets demonstrate the superior performance of the proposed method over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]