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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]

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]

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]

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]

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]

In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the "brute force" strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by computational tractability, and they are incomprehensible to the human modeller. Along the lines Levins described, this uncertainty generates a trade-off between realistic, precise models with predictive power and simple, highly idealised models that facilitate understanding. In addition to building ensembles of highly complex dynamical models, climate modellers can address model uncertainty by comparing models of different types, such as dynamical and data-driven models, and by systematically comparing models at different levels of what climate modellers call the model hierarchy. Despite its age, Levins' paper remains incredibly insightful and should be considered an important entry into the philosophy of computational modelling. [ABSTRACT FROM AUTHOR]

Subduction process is a dynamical bridge for the exchanges of heat between the atmosphere and subsurface ocean water, which is regarded as a central proxy for the ocean climate studies. Given its key indicator in climate signals, it is of importance to examine the ability of a model to simulate the global subduction rate before investigating the climate dynamics. In this paper, we evaluated the ability of 21 climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the subduction rate. In general, the simulation ability of the models to the subduction climatology is better than that to the long-term variation trend. Based on the comprehensive analysis of climatology distribution and long-term trend of the subduction rate, GISS-E2-1-G performs better in reproducing the subduction rate climatology and IPSL-CM6A-LR can simulate positive long-term trend for both the global mean subduction rate and the lateral induction term in the Antarctic Circumpolar Current (ACC) region. However, it is still challenging to capture both the distribution characteristics of the subduction climatology and the long-term temporal trend for the 21 CMIP6 models. In addition, the model results demonstrate that, the ACC area is the major region contributing to the long-term trend of the global mean subduction rate. The analysis in this paper indicates that the poor simulation ability of reproducing the long-term trend of global mean subduction rate might be attributed to the ocean dynamics, for example, the zonal velocity at the bottom mixed layer and zonal gradient of mixed layer depth. [ABSTRACT FROM AUTHOR]

As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this paper, I explain how supervised ML can be improved with better data ontology, or the way we make categories and turn information into data. More specifically, we should design data ontology in such a way that is consistent with the knowledge that we have about the target phenomenon so that such ontology can help us make the inductive leap. I do so by thinking through a thought experiment, Goodman's New Riddle of Induction (Fact, fiction, and forecast, Harvard University Press, 1955). Goodman's riddle helps flesh out three problems of induction: (1) the problem of equal goodies, that there are often too many equally good inductive results given the same data; (2) the problem of diverging performance, that these equally good results can give opposite predictions in the future; and (3) the problem of mediocrity, that when averaged across all equally possible datasets and tasks, no inductive algorithm outperforms any other. I show that all these three problems are manifested as real obstacles in ML practice, namely, the Rashomon effect (Breiman in Stat Sci 16(3):199–231, 2001), the problem of underspecification (D'Amour et al. in J Mach Learn Res, 2020, https://doi.org/10.48550/arXiv.2011.03395), and the No Free Lunch theorem (Wolpert in Neural Comput 8(7):1341–90, 1996, https://doi.org/10.1162/neco.1996.8.7.1341). Lastly, I argue that proper data ontology can help mitigate these problems and I demonstrate how using concrete examples from climate science. This research highlights the links between philosophers' discussions of induction and implications in ML practice. [ABSTRACT FROM AUTHOR]

Soil and other Earth scientists who conduct research on C management found themselves, in the past decade within a swirl of efforts concerning climate mitigation, economic and business investments in carbon markets, and political aspirations. All these external pressures are issues with which soil science is largely unfamiliar. As a result, science has responded without deeply considering the landscape in which it finds itself, and some of the unanticipated challenges these issues present. Here, we suggest soil C scientists now consider and respond to these issues. The first order challenge is to transition from the concept of technical carbon sequestration potentials, made in the absence of social and policy contexts, to societally achievable sequestration estimates based on highly transdisciplinary teams of natural and social sciences and scientists. To achieve this will requires re-thinking national science funding programs, in which climate-relevant social science is under-funded. In addition, the science of soil C itself is in need of a priority shift. Presently, publications in soil C sequestration out-strip papers on soil feedbacks to climate change, and on how to adapt soil to climate change: two areas of research which may well be more societal important in the next few decades than sequestering C. Most seriously, given the urgent nature of our collective societal climate problem, our profession must not find itself a decade from now continuing the now 20-year-old narrative that soil C can potentially mitigate climate change and compensate for greenhouse gas emissions. We must consider the possibility that other options and expenditures of resources are more viable, and we must reframe our science's objectives to expand into the many other urgent needs that confront humanity. [ABSTRACT FROM AUTHOR]

The integration of behavioral science into conservation science and climate science has enabled the development of both novel research questions and practical interventions. However, most behavioral interventions aim to change private, individual behaviors, rather than transform the political economic systems that drive current biodiversity, climate, and social crises. In this paper, we argue that closer collaboration between behavioral scientists working on biodiversity and climate issues, on the one hand, and advocates for radical alternatives to current political economic systems, on the other, could advance such needed systemic transformation. While the work of both groups is subject to some critique, we suggest that closer collaboration could enable the strengths of each to address the others' weaknesses. This complementarity is particularly true when behavioral interventions are co-designed with advocates and targeted towards powerful individuals whose behavior could affect systems-level transformation. We use the fossil fuel divestment movement as an illustrative example of one way in which this collaboration could be mutually beneficial, and then outline potential political, practical, and ethical implications that may accompany such collaborations in the biodiversity conservation and climate change fields. [ABSTRACT FROM AUTHOR]

Melting layer usually exists in precipitation where ice particles gradually melt into liquid particles. Utilizing the detection from the Dual-frequency Precipitation Radar onboard Global Precipitation Measurement Mission Core Observatory during 2018-2022, this paper investigates the quasi-global climatological features of Melting layer over 65°S ~65°N and analyzes the relationships with elevation in the golden case involving the section along 32.5°N across China. The distribution of Melting layer is latitude-dependent: Melting layer is higher in 30°S~30°N and decrease toward the mid-and high-latitude, which are generally lower in 30°S~65°S throughout the year. The height and thickness of Melting layer change more dramatically in mid-and high latitudes than in low latitudes for diurnal variations. The higher elevation terrain has the lower Melting layer top height than other terrains, yet the geometric thickness of Melting layer is also thinner. A golden case is selected to represent the analysis of Melting layer play role in rainfall. When the Melting layer grow thinner with steeply rising elevation due to the thermal change and orographic uplift, there is weak rain with a greater number of smaller raindrops in the golden case. However, Melting layer can get widen and raindrops can get sufficient growth as the elevation declines. [ABSTRACT FROM AUTHOR]

Climate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster. [ABSTRACT FROM AUTHOR]

The prediction of future weather patterns has recently become a very important research area due to the ongoing climate change process which causes extreme weather events and rapidly changing weather patterns. In this paper we compare the prediction accuracy of eight data-driven methods, which had been developed for time series prediction, on future weather patterns in central Croatia. The evaluated methods are Seasonal naïve, AutoRegressive Integrated Moving Average (ARIMA), Error-Trend-Seasonality (ETS), Exponential smoothing state space model with Box-Cox transformation (TBATS), Dynamic Harmonic Regression (DHR), Neural Network AutoRegression (NNAR), Support Vector Regression (SVR) and Long Short-Term Memory (LSTM). In our experimental evaluation, we use a historical data from 1961 to 2017 that contains temperature, air pressure and precipitation values for eight weather stations in central Croatia, and indices from two atmospheric oscillations, namely North Atlantic Oscillation (NAO) and Arctic Oscillation (AO). The results of our evaluation show that SVR is the best method, and that DHR and NNAR methods are also better than the other evaluated methods, as far as the accuracy of prediction is concerned. Among DHR and NNAR methods, DHR method is better for the prediction of temperature and air pressure, while NNAR method is better for the prediction of precipitation. Additionally, our evaluation shows that SVR, DHR and NNAR methods achieve a better prediction accuracy when oscillation indices are included as additional predictors. [ABSTRACT FROM AUTHOR]

With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of future precipitation patterns over the Lake Victoria Basin, East Africa, using bias-corrected CMIP6 model projections. A mean increase of about 5% in mean annual (ANN) and seasonal [March–May (MAM), June–August (JJA), and October–December (OND)] precipitation climatology is expected over the domain by mid-century (2040–2069). The changes intensify towards the end of the century (2070–2099) with an increase in mean precipitation of about 16% (ANN), 10% (MAM), and 18% (OND) expected, relative to the 1985–2014 baseline period. Additionally, the mean daily precipitation intensity (SDII), the maximum 5-day precipitation values (RX5Day), and the heavy precipitation events—represented by the width of the right tail distribution of precipitation (99p–90p)—show an increase of 16%, 29%, and 47%, respectively, by the end of the century. The projected changes have a substantial implication for the region—which is already experiencing conflicts over water and water-related resources. [ABSTRACT FROM AUTHOR]

Outdoor thermal comfort (OTC) has considerable impacts on the health and life of different groups of people. Older people are one of the most vulnerable groups to climate-related diseases. Heat-related deaths are likely to increase due to an increasing proportion of adults aged 65 and over in the total population. Reviewing literature shows that very few studies have investigated OTC for older people compared to indoor environments. This paper aims to present a comprehensive literature review of the recent literature that discusses differences in OTC sensations and preferences between older people and other age groups. Also, this research reviews effective parameters that should be considered during the planning of outdoor places. These parameters are mainly categorized into two groups of environment-and human-based parameters. Existing comfort models are not fully developed to consider the effects of both environment and human-based parameters for OTC assessment for older people. It is important that the ageing aspects of the OTC being taken into account to shed light on the complex factors of OTC studies. Therefore, holistic agendas and comprehensive masterplans can facilitate communication between policy-making entities, scientific institutions, and urban planners to present healthier urban development for the elderly. [ABSTRACT FROM AUTHOR]

We present a thorough analysis of the scientific production in climate change and adaptation in Brazil. We conducted a bibliometric and bibliographic review to reveal and discuss how climate change research has been carried out. We compared a broader climate change research dataset with a more specific climate adaptation research dataset, to understand the main differences and convergence points, and how science conducted from a mitigation and adaptation perspective demonstrates potential to confront the climate change challenges and drivers in Brazil. Four main clusters of investigation within climate change were detected: (1) impacts on forest and plant development, (2) land use and ecology, (3) adaptation/governance, and (4) climate/atmospheric studies. Only about 5% of the total studies on climate change address adaptation, for which three main clusters of research were identified: (1) adaptation actions and policies; (2) urban environment, vulnerability, and health and (3) food and coastal impacts. Although there are strong research trends in climate change for the Amazon Forest, there was less evidence of studies concerning climate adaptation for this and other Brazilian biomes, smaller cities, rural and traditional communities, and poorer regions. Our results shed a light on the more commonly chosen research topics, their strongest points and potential gaps and trends. This can contribute to the scientific communication and implementation of climate actions in Brazil, and a better understanding of the climate science knowledge from the perspective of a middle-income country. [ABSTRACT FROM AUTHOR]

As with many Indigenous Peoples, the Siberian Evenki nomadic reindeer herders and hunters have observed increasing consequences of climate change on the cryosphere and biodiversity. Since 2017, they have observed previously unthinkable changes in topography. Based exclusively on an Evenki Indigenous Ecological Knowledge system-social anthropology coproduction and community-based continuous observation from 2013, this paper analyses what a Subarctic People observes, knows, does not know, hypothesizes, and models (collectively or individually) about climate change impacts on Indigenous landscape types typical for local river systems. These landscapes are crucial tools for traditional activities. To the nomads, the landscape changes emerge from general anomalies: competition from new plant species; atmosphere–ground–vegetation interactions; icing blisters decrease; rising receding river water interactions; the formation of new soil, ice, and snow types; increasing ground, air, and water temperatures; and the (non)circulation of harsh air throughout the snowpack. We demonstrate the science-like structure and value of Indigenous typologies and hypotheses. [ABSTRACT FROM AUTHOR]

The existing F-test for two counts data from the Poisson distribution under classical statistics can be applied only when the counts in the data are exact or not intervals. The existing test cannot be applied when the count data is indeterminate, in the interval, and uncertain. In this paper, the F-test for two counts data from the Poisson distribution under classical statistics is designed. The test for two counts recording at the same time or different times is presented. The daily and the monthly number of records broken data in the U.S from the weather department is selected for the application of the proposed test. The application and comparison studies show the efficiency of the proposed test. The proposed test was found to be informative, flexible, and appropriate to be applied in an uncertain environment. [ABSTRACT FROM AUTHOR]

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country's economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes. [ABSTRACT FROM AUTHOR]

The level of solar activity and its influence on ionospheric variability has received a focal interest among the ionospheric community. This paper examines the impact of long-term solar activity on the ionosphere over an African equatorial global navigation satellite system (GNSS) station based on a large database of solar and ionospheric datasets. The ionospheric total electron content (TEC) periodic variations, climatology and its relation to solar activity are investigated through wavelet analyses. It is observed that the magnitude and intensity of TEC fluctuations measured by GNSS receivers vary substantially with solar radiation intensity. To probe the processes accountable for the TEC periodicities in the region, the wavelet analysis is undertaken by considering solar indices (solar radio flux and sunspot number) and observed TEC values. The results show a clear strong periodicity of 27 days in the ionospheric TEC, sunspots and F10.7 solar flux power spectra, associated with the sun's 27 days rotation period. The ultraviolet ray intensity and solar ionization were lower during the solar maximum period (2013 to 2014) of solar cycle 24 compared to the solar maximum period (2001 to 2002) of solar cycle 23, which is the key factor for driving the decreasing trend in TEC between 1999 and 2017. The ionospheric and solar climatology analysis indicates that ionospheric TEC behaviour could be a good indicator of long-term solar activity trend. A large mass of Africa lies within the equatorial and low latitudes making the ionosphere over the region being highly susceptible to low latitude electrodynamics. Results from this study would support the concerted efforts towards developing a reliable regional ionospheric forecasting model for technological applications relying on spaced-based navigation and satellite services. [ABSTRACT FROM AUTHOR]

This paper reviews the results of climate change science activities that have been promoted as part of crisis management for climate change to date and draws lessons from them to further strengthen responses to climate stabilization and suggests issues that the scientific community should address going forward. The global climate is a complex physical phenomenon, and it takes time to elucidate its mechanisms. In addition, due to its nature as a global public good with no alternatives, it cannot be controlled without the concerted consensus and action of all nations. Already, the climate management system on a global scale is lagging behind and has not caught up with the actual progress of climate change, and there are concerns that the "climate crisis" will undermine human sustainability. The science of climate change, which is the vanguard of sustainability science, is tackling this unprecedented challenge for humanity by learning by doing and has almost finished collecting nature-side knowledge for stabilizing climate. The next big challenge is how to motivate human society to achieve a carbon-neutral world. Given the limited time available, calls to action on the ground by stakeholders and citizens to reduce greenhouse gases, in parallel with the implementation of top-down policies, will be crucial. It is hoped that crisis management will be strengthened through the consolidation of human knowledge that crosses and integrates various fields. When the science of climate change succeeds in stabilizing the climate, it will serve as a good reference for subsequent sustainability science. [ABSTRACT FROM AUTHOR]

Coherent structures (CSs), which are the most often understood as time-stable vortices, play an important role in the Earth's climate system, since they make a significant contribution to the processes of momentum and heat and mass transfer of any fluid medium, including the atmosphere and the ocean. This is true for all scales of motion, but is particularly important for more chaotic mesoscale processes. A reliable methodology for identifying mesoscale vortices can potentially stimulate the development of a deterministic climatology of mesoscale processes. The main difficulty in developing this direction is the absence of a rigorously mathematical definition of a vortex, which makes it impossible to automatically search for CSs in spatial data. At the same time, the increase in the spatial resolution of numerical models makes this problem increasingly urgent. Some developments in this direction have been carried out in the field of small-scale turbulence: a number of criteria have been developed that allow for the identification of vortices with varying degrees of reliability. In this paper, the applicability of the three most popular criteria for CS identification in significantly larger-scale geophysical data is studied. [ABSTRACT FROM AUTHOR]

The applicability of the four-parameter (4P) kappa distribution to fit the annual maximum (AM) and the peak over threshold (POT) extreme precipitation series in the Amman Zarqa Basin (AZB), Jordan, is assessed. This paper presents a practical, easy-to-implement, yet accurate method in light of the method of L-moments for parameter estimation of 4P kappa. Based on the results from the data analysis of daily precipitation records during a period that exceeded 50 years at 23 weather stations, the 4P kappa distribution can fit the precipitation extremes for both the AM and POT series for each weather station well. The 4P kappa distribution can be employed in place of its three-parameter distributions typically selected for simulating precipitation extremes (i.e., generalized extreme value (GEV), generalized logistic (GLO) distribution for AM series, and generalized Pareto (GP) distribution for POT series). The bootstrap resampling for performance evaluation beyond the dataset length shows that the 4P kappa distribution outperforms its three special cases (GLO, GEV, and GP distributions) in the context of modeling the AM and POT series for all sampling sizes. Additionally, in representing the distribution's upper right tail quantile estimations, the 4P kappa distribution performed better and, in some circumstances, was comparable to its three special cases (GLO, GEV, and GP distributions). The outcomes of this study and the method employed should lay the groundwork for wider implementation of the 4P kappa distribution in hydrological and climate science applications, as well as in other disciplines. [ABSTRACT FROM AUTHOR]

This paper presents a synthesis of the main characteristics of precipitation in the State of Rio de Janeiro (Brazil) based on extreme rainfall indicators. Daily precipitation data are derived from 56 rainfall stations during the second half of the twentieth century and the 2000s. Eight indices related to extreme precipitation were analyzed. The Mann–Kendall nonparametric test and the Sen's Curvature were employed to evaluate the significance and magnitude of trends. The primary climatological aspects and identified trends throughout the last decades are discussed, besides the hydrometeorological impacts associated with them. Lower values of annual total precipitation are recorded in northern Rio de Janeiro (around 800 mm) and higher in the southern State (up to 2,200 mm). The Serra do Mar affects the frequency of heavy precipitation, and the areas near the sea and high relief present the highest values of consecutive days with expressive rainfall (more than 150 mm in 5 days). These areas also showed a high concentration of flood and landslides events. Most of Rio de Janeiro exhibits precipitation intensity of about 13 mm/day. The maximum number of consecutive dry days shows a gradient from the coast (about 30 days) to the State's interior (around 50 days). Regarding trends, there is a growth of accumulated extreme precipitation in various stations near the ocean. The extreme rainfall in 24 h displays an increase in most Rio de Janeiro (+ 1 to + 5 mm/decade). The consecutive dry and rainy days present similar signs of decreasing trends, suggesting irregularly distributed precipitation in the State. This study is especially relevant for decision-makers who need detailed information in the short and long term to prevent natural hazards like floods and landslides and the related impacts in the environmental and socioeconomic sectors of the Rio de Janeiro. [ABSTRACT FROM AUTHOR]

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The present paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC (Intergovernmental Panels on Climate Change) in Brazil. We used temperature and precipitation data from 4942 cities collected on the NASA/POWER platform (National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources) from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041–2060 and 2061–2080 associated with four IPCC climate change scenarios. The spatialization of the data occurred according to the bioclimatic classes designed to meet the thermal and water needs of the crop. In the current scenario, class B5 has a predominance of 37.07% of the country, characterizing the Midwest, Southeast, and Northeast regions, as well as the state of Paraná, as suitable for the cultivation of pitaya. Projections of temperature increase and reduction of accumulated rainfall were found throughout Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction of areas suitable for the cultivation of pitaya with a greater predominance of classes B8 and B9. In the South and Southeast regions, class B5 makes up a large part of the areas that remained suitable for the production of this fruit until 2080. The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas suitable for economic production are restricted. [ABSTRACT FROM AUTHOR]

This research focuses on a bibliometric analysis of research on aerosols' impact on the glaciers in the Himalayan glacier region published in journals from all subject categories based on the Science Citation Index Expanded, collected from the Web of Science and Scopus database between January 2002 and April 2022. The indexing phrases like "aerosol," "glacier," and "snow" are commonly used terms and have been utilized to collect the related publications for this investigation. The document selections were based on years of publication, authorship, the scientific output of authors, distribution of publication by country, categories of the subjects, and names of journals in which scholarly papers were published. The number of articles on aerosols accelerating the melting of glaciers shows a notable increase in recent years, along with more glacier melting results from countries involved in climate science research. People's Republic of China (382) was the country with the highest publication output on aerosols impacting the melting of glaciers. The USA (367) was the most cited country, with about 17,500 total citations and 80.40 average citations per year from January 2002 to April 2022. The results reveal that research trends in the glaciers on aerosols' impact on the glaciers have been attractive in recent years, and the number of articles in this field keeps increasing fast. This study offers opportunities to track research trends, identify collaboration prospects, and inform climate policy. Integrating data sources and engaging the public will further enhance the impact and relevance of this critical research field. [ABSTRACT FROM AUTHOR]

Compound extreme natural events cause a significantly larger impact than individual extreme events. Therefore, the urgency of exploring the climatology of compound events is growing. This paper is aimed to identify the current hotspots of compound heatwaves and droughts (CHD) and trends in their occurrence in southeast Australia. In this context, 61 weather stations were selected from the study area, and analyses were carried out over the extended summer season of the time period 1971–2021. The hotspots of CHDs in southeast Australia were identified considering both the total count of CHD days and 90th percentile of CHDs during the study period. The study period was divided into two periods 1971–2000 and 2001–2021, to assess the change in hotspots spatially and temporally. Four different attributes of CHDs based on the number, duration, severity and amplitude of CHDs were also calculated, and Mann–Kendall (MK) test followed by Sen's slope was adopted to detect the trends in all these four attributes of CHDs. Trends in CHD indices were also calculated for the two periods of 1971–2000 and 2001–2021. For calculating the CHD, excess heat factor (EHF) was used to identify the heatwaves. In the case of drought identification, SPEI and SPI drought indices were adopted with aggregation of 3 and 126 antecedent months, and three different threshold values were selected to consider three levels of dry conditions, e.g. 0, − 0.5 and − 1.0. It has been found that more CHDs occurred on the eastern side of NSW and Queensland states. Furthermore, the total count of CHD days increased notably during the last two decades. The trends in CHD indices were found to be significant in the recent period (2001–2021), and there was no trend in the earlier period (1971–2000). The findings of this study will help to plan heat and drought-related emergency management in the study area. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) that reach the complex terrain of High Mountain Asia (HMA) cause significant hydrological impacts for millions of people. While ARs are often associated with precipitation extremes and can cause floods and debris flows affecting populated communities, little is known about ARs that reach as far inland as HMA. This paper characterizes AR types and investigates dynamical mechanisms associated with the development of ARs that typically affect HMA. Combined empirical orthogonal function (cEOF) analysis using integrated water vapor transport (IVT) is applied to days where an AR reaches HMA. K-means cluster analysis applied to the first two principal components uncovered three subtypes of AR events with distinct synoptic characteristics during winter and spring months. The first subtype increases precipitation and IVT in Western HMA and is associated with a zonally oriented wave train propagating within the westerly jet waveguide. The second subtype is associated with enhanced southwesterly IVT, anomalous upper-level cyclonic circulation centered on 45 ∘ E, and precipitation in Northwestern HMA. The third subtype shows anomalous precipitation in Eastern HMA and southwesterly IVT across the Bay of Bengal. Interannual variations in the frequency of HMA ARs and relationships with various teleconnection patterns show that western HMA AR subtypes are sensitive to well-known remote large-scale climate factors, such as the El Niño Southern Oscillation, Arctic Oscillation, and the Siberian High. These results provide synoptic characterization of the three types of ARs that reach HMA and reveal the previously unexplored significance of their contribution to winter and spring precipitation. [ABSTRACT FROM AUTHOR]

The ℓ 1 trend filtering enables us to estimate a continuous piecewise linear trend of univariate time series. This filter and its variants have subsequently been applied in various fields, including astronomy, climatology, economics, electronics, environmental science, finance, and geophysics. Although the ℓ 1 trend filtering can estimate a continuous piecewise linear trend of univariate time series, it cannot estimate a common continuous piecewise linear trend of multiple time series. This paper develops a statistical procedure that enables us to estimate it, which is a multivariate extension of the ℓ 1 trend filtering. We provide an algorithm for estimating it and a clue to specify the tuning parameter of the procedure, both required for its application. We also (i) numerically illustrate how well the algorithm works, (ii) provide an empirical illustration, and (iii) introduce a generalization of our novel method. [ABSTRACT FROM AUTHOR]

The scientific evidence of climate change has never been clearer and more convergent, and calls for transformations to sustainability have never been greater. Yet, perspectives and social opinions about it remain fractured, and collaborative action is faltering. Climate policy seeks to forge a singular sense of climate change, dominated by an 'information deficit model' that focuses on transferring climate science to the lay public. Critics argue that this leaves out certain perspectives, including the plurality of meanings uncovered through participatory approaches. However, questions remain about how these approaches can better account for nuances in the psychological complexity of climate change, without getting stuck in the cul-de-sacs of epistemological relativism and post-truth politics. In this paper, I explore an approach through which we might find shared meaning at the interface of individual and collective views about climate change. I first present a conceptual framework that describes five psychological reasons why climate change challenges individual and collective meaning-making, and also provides a way to understand how meaning is organized within that. I then use this framework to inform the use of photo voice as a transformative (action-research) method, examining its ability to overcome some of the meaning-making challenges specific to climate change. I discuss how participants from a coffee cooperative in Guatemala reflected first on their own climate meanings and then engaged in a meaning-making process with other actors in the coffee value chain. Findings suggest a psychosocial approach to climate engagement—one that engages both subjectively and intersubjectively on the complexities unique to climate change—is helpful in acknowledging an ontological pluralism of 'climate changes' amongst individuals, while also supporting a nexus-agreement collectively. This may in turn contribute to a more effective and ethical process of transformation. [ABSTRACT FROM AUTHOR]

The Ångström–Prescott equation defines generically the relationship between solar energy available at ground level and sunshine duration. From the very beginning in the history of the solar energy converters, the equation was extensively used to estimate the amount of collectable solar energy. In the present paper, we propose a different way of looking to the Ångström–Prescott equation, namely, to use it for the estimation of cloud and clear sky atmospheric transmittances. The key tools are the mathematical expressions which give the clear sky and cloud transmittances in terms of the Ångström–Prescott empirical coefficients. The theory is verified by comparing the mean all-sky atmospheric transmittance (innovatively inferred from the cloud and clear sky atmospheric transmittances) with the measured clearness index. Reasonably good concordance between the two quantities has been found. It was shown that the best performance is obtained with the linear Ångström–Prescott equation. Higher-order polynomial equations tend to overfit the measurements. Examples are also shown about how to use the linear Ångström–Prescott equation to obtain information about the time series stationarity and the seasonal sensitivity of the cloud and clear-sky transmittances. The present point of view gives the perspective to estimate past cloud and clear-sky climatology in places where the Ångström–Prescott equation has been fitted to the sunshine data. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are important warm-season precipitation systems in eastern China. However, our knowledge of their climatology and capability in their simulation is still insufficient. This paper examines their characteristics over the 2008–2017 warm seasons using convection-permitting climate simulations (CPCSs) with a 3-km grid spacing that explicitly resolves MCSs, as well as a high-resolution gauge-satellite merged precipitation product. An object-based tracking algorithm is applied to identify MCSs. Results indicate that the MCS genesis and occurrence are closely related to the progression of the East Asian monsoon and are modulated by the underlying topography. On average, about 243 MCSs are observed each season and contribute 19% and 47% to total and extreme warm-season precipitation. The climatological attributes and variabilities are reasonably reproduced in the CPCS. The major model deficiencies are excessive small MCS occurrence and overmuch MCS rainfall, consequently overestimating the precipitation contributions, whereas observational uncertainties may play a role too. Both the observed and simulated MCS precipitation feature a nocturnal or morning maximum and an eastward delayed diurnal peak east of the Tibetan Plateau, in contrast to the dominant afternoon peak of non-MCS precipitation. The favorable comparison with observations demonstrates the capability of CPCSs in simulating MCSs in the Asian monsoon climate, and its usefulness in projecting the future changes of MCSs under global warming. The finding that non-MCS precipitation is responsible for the high biased afternoon precipitation provides helpful guidance for further model improvement. [ABSTRACT FROM AUTHOR]

In this paper we consider some questions surrounding whether or not regional climate models "add value," a controversial issue in climate science today. We highlight some objections frequently made about regional climate models both within and outside the community of modelers, including several claims that regional climate models do not "add value." We show that there are a number of issues involved in the latter claims, the primary ones centering on the fact that different research questions are being pursued by the modelers making the complaints against regional climate models. Further issues focus on historical deficiencies of particular—but not generalizable—failures of individual regional models. We provide tools to sort out these different research questions and particular failures, and to improve communication and understanding surrounding added value in climate modeling and philosophy of climate science. [ABSTRACT FROM AUTHOR]

Migration flows from a southern European country to another one have received scarce attention so far. This is especially true for Italians migrating to Greece or, more specifically, the Athens area. Thus, there are limited insights as to the reasons why Italians are leaving and why they have been choosing Greece as their destination. This paper looks at their motives for migrating and their destination choice in order to understand the diversity of migratory trajectories through a typology. In order to do this, we carried out in-depth interviews to Italians living, both permanently and temporarily, in the Athens area, employing snowball sampling. As a result, we have identified 5 types of Italian immigrants in Athens: Mediterranean, nomadic, work, entrepreneurial, and marriage migrants. Mediterranean migrants are driven by the typically Mediterranean character of climate, landscape, food, and culture in their deliberate choice of Athens. Nomadic migrants have casually chosen Athens to satisfy their need of continuous physical mobility and multiple moorings as a defining aspect of their identity. Work migrants are motivated by the search of a job regardless of the place and work content. Entrepreneurial migrants are motivated by a vocation for a professional career in Athens. Finally, for marriage migrants, the choice of Athens is a consequence of a couple choice and shared life projects. [ABSTRACT FROM AUTHOR]

There is an increasing number of studies focusing on extreme weather all over the world, but global trends and research topics related to extreme weather are still unclear. This study aimed to explore the current situation, research themes, and future trends in the field of extreme weather. Publications published from 1980 to 2019 were identified and retrieved from the Web of Science Core Collection databases by using keywords on May 5, 2020. Excel 2019, VOSviewer, R, and CiteSpace were used for scientific analysis. The results showed that (1) the number of publications on extreme weather research has rapidly increased and expanded continually, shifting from core disciplines to interdisciplinary fields; (2) the International Journal of Climatology was the most productive journal, and climate and environment were the most popular subject categories. Most studies were carried out in the USA, China, Germany, and other nations, and the Chinese Academy of Sciences was the most productive institution; (3) the main research topics were summarized as (a) climate change; (b) variability; (c) trends; (d) rainfall; (e) temperature; and (f) maximum. At the same time, (4) keyword bursts analysis showed that the domain focused on changes to atmospheric rivers, the impacts of global nitrogen content on extreme weather, and the relationship between water quality, soil moisture content, and extreme weather. Based on the in-depth analysis of extreme weather research, this paper developed a further understanding of the developments in this field over the past 39 years and also provided a reference for future research. [ABSTRACT FROM AUTHOR]

We welcome a broader debate on this aspect of global responses to climate change, attempt to reconcile our views in the following. B replying to b A. Lanson et al. I Nature Communications i https://doi.org/10.1038/s41467-022-31425-x (2022) In response to our recent paper[1] (hereafter SFL20) concerning the relation between natural variability in the climate system and the time to detect a response to emissions mitigation, Lanson et al.[2] (hereafter L22) call for 'a broader debate on how to best assess and communicate emerging effects of climate mitigation in the light of natural variability'. It is still efficient in revealing differences between the rapid climate response to aerosol perturbations, similar to the idealized test performed by L22, and the slower evolution from e.g. CO2 emissions. [Extracted from the article]

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Climate variability in the growing season is well suited for testing adaptation measures. Adaptation to adverse events, such as heatwaves and droughts, increases the capacity of players in agri-food systems, not only producers but also transporters and food manufacturers, to prepare for production disruptions due to seasonal extremes and climate change. Climate impact models (e.g., crop models) can be used to develop adaptation responses. To run these models, historical records and climate forecasts need to be combined as a single daily time series. We introduce the daily 0.5° global hybrid reanalysis-forecast meteorological forcing dataset from 2010 to 2021. The dataset consists of the Japanese 55-yr Reanalysis (JRA55) and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI-CPS2) 5-member ensemble forecast. Both are bias-corrected using the Delta method and integrated with a baseline climatology derived from the Environmental Research and Technology Development Fund's Strategic Research 14 Meteorological Forcing Dataset (S14FD). The dataset is called JCDS (JRA55-CPS2-Delta-S14FD) and offers a framework for monitoring and forecasting applications towards adaptation. [ABSTRACT FROM AUTHOR]

A climatology of stratospheric gravity waves (SGWs) induced by tropical cyclones (TC-SGWs) is necessary for stratospheric aviation safety and accurate numerical weather prediction. Few previous works have characterized the climatology of TC-SGWs, and the relative importance of the source and background wind for TC-SGWs has not been clearly established. Here we present the distribution of TC-SGWs formed over the northwest Pacific Ocean based on 30 years of ERA5 reanalysis data. Using explainable artificial intelligence, we found the background wind in the lower stratosphere to be more important than TCs in controlling the distribution pattern of TC-SGWs. The background wind shear can influence the direction of TC-SGW propagation by refraction and filtering, which influences the distribution pattern of the TC-SGWs. The TC intensity only influences the TC-SGW intensity. Our results provide information on TC-SGW hotspots and suggest the importance of the background wind shear. [ABSTRACT FROM AUTHOR]

In Southeast Asia, projections of rapid urban growth coupled with high water-related risks call for large investments in infrastructure—including in blue–green infrastructure (BGI) such as forests, parks, or vegetated engineered systems. However, most of the knowledge on BGI is produced in the global North, overlooking the diversity of urban contexts globally. Here, we review the literature on BGI for flood risk mitigation and water quality improvement in Southeast Asian cities to understand the scope of practical knowledge and identify research needs. We searched for evidence of local types of BGI in peer-reviewed and grey literature and assessed the performance of BGI based on hydrological, societal, and environmental metrics. The body of literature on BGI in Southeast Asia is small and dominated by wealthier countries but we found evidence of uptake among researchers and practitioners in most countries. Bioretention systems, constructed wetlands, and green cover received the most attention in research. Evidence from modelling and laboratory studies confirmed the potential for BGI to address flooding and water quality issues in the region. However, practical knowledge to mainstream the implementation of BGI remains limited, with insufficient primary hydrological data and information on societal and environmental impacts. In addition, the performance of BGI in combination with grey infrastructure, under climate change, or in informal settlements is poorly studied. Future research and practice should focus on producing and sharing empirical data, ultimately increasing the regional knowledge base to promote efficient BGI strategies. [ABSTRACT FROM AUTHOR]

Whilst the TC has been fully independent of the IPCC, there has been significant engagement with the IPCC throughout the TC, resulting in a number of contributions lead- or co-authored by IPCC staff. This article is part of the topical collection "Climate Change Communication and the IPCC", edited by Saffron O'Neill and Roz Pidcock The Intergovernmental Panel on Climate Change (IPCC) is widely regarded as the most important and authoritative source on climate change, its impacts and how to tackle the rising emissions that drive it. In 2016, the IPCC convened an Expert Meeting on Communication, which led to a number of recommendations to enhance IPCC communications activities, strategy and capacity (IPCC [1]; see also Lynn and Peeva, this issue). Introducing the Topical Collection: "Climate change communication and the IPCC". [Extracted from the article]

Today, more than 70 carbon pricing schemes have been implemented around the globe, but their contributions to emissions reductions remains a subject of heated debate in science and policy. Here we assess the effectiveness of carbon pricing in reducing emissions using a rigorous, machine-learning assisted systematic review and meta-analysis. Based on 483 effect sizes extracted from 80 causal ex-post evaluations across 21 carbon pricing schemes, we find that introducing a carbon price has yielded immediate and substantial emission reductions for at least 17 of these policies, despite the low level of prices in most instances. Statistically significant emissions reductions range between –5% to –21% across the schemes (–4% to –15% after correcting for publication bias). Our study highlights critical evidence gaps with regard to dozens of unevaluated carbon pricing schemes and the price elasticity of emissions reductions. More rigorous synthesis of carbon pricing and other climate policies is required across a range of outcomes to advance our understanding of "what works" and accelerate learning on climate solutions in science and policy. Carbon pricing policies are adopted in many countries around the world to mitigate climate change. This systematic review shows that significant emission reductions of between 5 and 21% are achieved by at least 17 out of 21 reviewed policy schemes. [ABSTRACT FROM AUTHOR]

This study analyzed smallholder maize farmers' perceptions of climate change vulnerability and adaptation strategies in the eastern Democratic Republic of the Congo. The study used a cross-sectional quantitative approach, with smallholder farmers selected based on farm size (less than 10 hectares). Using simple random sampling techniques, 210 smallholder farmers in South Kivu Province was selected from October and November 2021. The data were analyzed using frequency analysis, non-parametric Mann–Whitney U-tests, Tobit regression model and structural equations modeling, which demonstrated that demographic and socioeconomic factors influenced smallholder farmers' perceptions and adaptation strategies to climate change. The findings revealed that the level of education, size of the field, and activities of smallholder farmers had significant effects on climate change perception and adaptation strategies. Smallholder farmers' perceptions had a negative (p < 1%) influence on maize smallholder farmers' adaptation to climate change. Common efforts should be made to encourage smallholder farmers to feel committed enough to implement climate change adaptation measures that are effective in mitigating or preventing climate change risk. The results of this study would have implications for policies to support maize smallholder farmers in their efforts to mitigate and resilient to climate change in sub-Saharan Africa. Firstly, the promotion of climate literacy must be sufficient to provide farmers with information on climate and forecasting. Secondly, to empower smallholder farmers with means and resources to prevent and reduce the effects of climate change. Lastly, enhancing the current environment for the agriculture sector will advance community inclusivity and food security. [ABSTRACT FROM AUTHOR]

The Andaman Sea has been a classic study region for internal solitary waves (ISWs) for several decades, and extraordinarily large ISWs are characteristic of the Andaman Sea in the Indian Ocean. This paper presents results on the estimation of the propagation velocity of ISWs in the Andaman Sea that were tracked using 195 image pairs acquired by MODIS National Aeronautics and Space Administration (NASA) Terra/Aqua satellites between January 2014 and December 2018. A total of 562 ISWs were identified during the period, and the results of the propagation velocity distribution of ISWs in the Andaman Sea are presented. The estimated propagation velocity of ISWs agrees well with the theoretical results derived from the Korteweg-de Vries (KdV) equation using monthly climatology stratification data and local bathymetry. The ISW propagation velocity decreases as they propagate from deep to shallow water; the maximum propagation velocity of 3.27 m/s was estimated on the western side of the Nicobar Islands and minimum speed of 0.54 m/s occurred in the shallow water region of the southeastern Andaman Sea. The results show that the ISW propagation characteristics differ in the northern, central, and southern regions of the Andaman Sea. In the northern Andaman Sea, the velocity of ISWs propagating westward was greater than that of ISWs propagating eastward at the same water depth. In the central Andaman Sea, the propagation velocity of the ISWs differed over a small area at a depth of 2 500 m, and the velocity of ISWs in the deep mixing layer in winter was higher than that in the shallow mixing layer in spring. Monthly variations in ISW propagation velocity were analyzed in the southern Andaman Sea, and the velocity of ISWs differed greatly in shallow water and was not significantly different in deep water. Water depth and monthly stratification play vital roles in controlling the phase speed of ISWs in the Andaman Sea. This study will provide a basis for the propagation and prediction of ISWs in the Andaman Sea. [ABSTRACT FROM AUTHOR]

Using data from Twitter (now X), this study deploys artificial intelligence (AI) and network analysis to map and profile climate change denialism across the United States. We estimate that 14.8% of Americans do not believe in climate change. This denialism is highest in the central and southern U.S. However, it also persists in clusters within states (e.g., California) where belief in climate change is high. Political affiliation has the strongest correlation, followed by level of education, COVID-19 vaccination rates, carbon intensity of the regional economy, and income. The analysis reveals how a coordinated social media network uses periodic events, such as cold weather and climate conferences, to sow disbelief about climate change and science, in general. Donald Trump was the strongest influencer in this network, followed by conservative media outlets and right-wing activists. As a form of knowledge vulnerability, climate denialism renders communities unprepared to take steps to increase resilience. As with other forms of misinformation, social media companies (e.g., X, Facebook, YouTube, TikTok) should flag accounts that spread falsehoods about climate change and collaborate on targeted educational campaigns. [ABSTRACT FROM AUTHOR]

The commonly used precipitation-based drought indices typically rely on probability distribution functions that can be suitable when the data exhibit minimal discrepancies. However, in arid and semi-arid regions, the precipitation data often display significant discrepancies due to highly irregular rainfall patterns. Consequently, imposing any probability distributions on the data for drought analysis in such regions may not be effective. To address this issue, this study employs a novel drought index called the Discrepancy Precipitation Index (DPI), specifically designed for arid regions. Unlike traditional methods, the DPI does not impose a probability distribution on the precipitation data; instead, it relies on the discrepancy between the data and the mean value. Drought severity classifications (i.e., Drought-I, Drought-II, and Drought-III) are proposed based on the DPI values. The DPI is used to characterize and assess the meteorological drought years based on annual and monsoonal precipitation over nineteen districts in Western Rajasthan, India, during 1901–2019. Additionally, a novel statistic called Discrepancy Measure (DM) is employed to assess the degree of discrepancy in the precipitation climatology of the districts for annual and monsoon precipitation time series. Based on annual precipitation, Jaisalmer district exhibited the highest number of historical drought years (35), whereas three districts, i.e., Jhunjhunu, Dausa, and Bhilwara exhibited the lowest number of drought years (11). Similarly, based on monsoon precipitation, Jaisalmer and Bhilwara encountered the highest (34) and the lowest (11) number of drought years, respectively. The return period of Drought-II is lower for monsoon precipitation-based DPI as compared to that of the annual precipitation-based DPI for all the districts. The DM and DPI-based total number of droughts are found to be strongly correlated for both annual and monsoon precipitation. The DM value is highest for Jaisalmer and lowest for Bhilwara district. The findings reveal DPI as an efficient tool for assessing drought years, particularly in arid climatic conditions. Moreover, as the DM value increases for a precipitation series, the DPI becomes more effective in capturing drought events. [ABSTRACT FROM AUTHOR]