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Results of 3-year full-scale field climatic tests on application of five types of inhibiting papers for anticorrosion protection of ferrous and nonferrous metals during storage in a humid tropical climate have been carried out by the Joint Russian and Vietnamese Tropical Research and Technology Center and presented here. It has been shown that the inhibiting paper IPCAS-118 can be recommended for anticorrosion protection of equipment made of steel, copper, and aluminum during 3-year storage in sheds in humid tropical climate conditions [ABSTRACT FROM AUTHOR]

The STatistical Analogue Resampling Scheme (STARS) statistical approach was recently used to project changes of climate variables in Germany corresponding to a supposed degree of warming. We show by theoretical and empirical analysis that STARS simply transforms interannual gradients between warmer and cooler seasons into climate trends. According to STARS projections, summers in Germany will inevitably become dryer and winters wetter under global warming. Due to the dominance of negative interannual correlations between precipitation and temperature during the year, STARS has a tendency to generate a net annual decrease in precipitation under mean German conditions. Furthermore, according to STARS, the annual level of global radiation would increase in Germany. STARS can be still used, e.g., for generating scenarios in vulnerability and uncertainty studies. However, it is not suitable as a climate downscaling tool to access risks following from changing climate for a finer than general circulation model (GCM) spatial scale. [ABSTRACT FROM AUTHOR]

The purpose of the present paper is to examine a selection of macro- and micro-linguistic features (at text and sentence/word level respectively) of the South-African Green Paper 'National Climate Change Response' from 2010. Our overarching assumption is that the Green Paper needs to handle competing interests, beliefs and voices in a narrative structure favouring specific courses of action. How does the government portray the complex natural and societal phenomenon of climate change, and how does it take into account the many and often competing national and international views and interests which come into play? Our hypothesis is that the Green Paper constructs a narrative and that it relates to a number of voices other than that of the authors, through linguistic markers of polyphony, such as negation, sentence connectives, adverbs and reported speech. Thus we propose a narrative and polyphonic analysis of the Green Paper, at the level of the text as a whole (macro-level) but also with attention to linguistic constructions of polyphony or 'multi-voicedness' (micro-level). We find that the narrative-polyphonic properties of the Green Paper contribute to a strategy for building consensus on climate change policy. The South African government assumes the role of main hero in its own climate change 'story', and there are subtle forms of interaction with different and typically non-identified voices, such as concessive constructions and presuppositions. These results support our overarching interpretation of the whole document as striving to impose a South African consensus on the issue of climate change. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the paper "On the Taboo on Researching in the Field of Global Climate Geoengineering," by A. G. Ryaboshapko, published in a previous issue of "Russian Meteorology & Hydrology" journal.

Change in groundwater dynamics (in terms of changes in depth to watertable and its salinity) is a key environmental concern for agricultural production using pulp and paper mill effluent for irrigation purposes. At the study site, the treated effluent is delivered from the mill into a winter storage dam. This storage dam is also meant to provide an opportunity for runoff collection and recycling for irrigated areas. A natural creek also exists along the farm boundary. This paper presents, using field observation data and computer simulation results, the impact of using treated effluent from the pulp and paper mill on groundwater dynamics at the farm (covering areas both under and outside the effluent irrigation paddocks); and on the flows in the adjacent creek. The modeling results show that after 5 years of operations, the change in aquifer storage is more under average climatic conditions (−23.5 mm/year) as compared to −7.1 and −9.0 mm/year under dry and wet climatic conditions, .respectively. Under average climatic conditions, the combined effect of irrigation and rainfall creates more hydraulic gradient towards the creek thereby depleting the aquifer storage more as compared to wet and dry climatic conditions. Resultantly, the subsurface groundwater flows towards the creek becomes around 57.9 mm/year under average scenario as compared to 55.0 and 36.7 mm/year under wet and dry climatic conditions, respectively. During the average climatic condition, 456.6 mm evaporation from shallow groundwater was estimated under the current management practices; which was reduced to 399.1 mm under the best management practices due to better use of all sources of water and capillary upflow from shallow groundwater. Thus, with the adoption of best management practices, there would be less risk of salinisation due to evaporation from shallow groundwater tables. [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]

One of the problems to affect Portland cement matrices is low resistance to aggressive agents, due principally to the presence of a high content of portlandite in the hydrated cements. Pozzolanic materials have, for decades, played an important role in improving the durability of cement-based materials. This work studies the behaviour of cement mortar matrices blended with 10% calcined paper sludge (source for metakaolin, MK) and exposed to different environmental conditions (marine and tableland environments). The results obtained using X-ray diffraction and scanning electron microscopy/energy-dispersive X-ray analyser techniques show that the ions present speed of different penetration as well as various phases and/compounds in the matrices following exposure over 1 year. [ABSTRACT FROM AUTHOR]

Among papers stating a position on anthropogenic global warming (AGW), 97 % endorse AGW. What is happening with the 2 % of papers that reject AGW? We examine a selection of papers rejecting AGW. An analytical tool has been developed to replicate and test the results and methods used in these studies; our replication reveals a number of methodological flaws, and a pattern of common mistakes emerges that is not visible when looking at single isolated cases. Thus, real-life scientific disputes in some cases can be resolved, and we can learn from mistakes. A common denominator seems to be missing contextual information or ignoring information that does not fit the conclusions, be it other relevant work or related geophysical data. In many cases, shortcomings are due to insufficient model evaluation, leading to results that are not universally valid but rather are an artifact of a particular experimental setup. Other typical weaknesses include false dichotomies, inappropriate statistical methods, or basing conclusions on misconceived or incomplete physics. We also argue that science is never settled and that both mainstream and contrarian papers must be subject to sustained scrutiny. The merit of replication is highlighted and we discuss how the quality of the scientific literature may benefit from replication. [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]

Our News in Brief series is designed to offer different views revolving around the earth sciences, as various experts will be invited to provide a brief look around the recent research conducted in their area. The selection of research papers is left up to each expert and due to the broadness of each field is not intended to be a comprehensive overview. Links to the published work are provided in each section. In this issue we have invited Stephanie Hänsel, a postdoctoral senior researcher at the Interdisciplinary Environmental Research Center in Freiberg Germany, to select and summarize some of the latest findings in climate science. In this second article of our series, Hänsel gives a brief rundown on five research papers of global relevance. [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]

Contributions to the climate governance literature have highlighted the importance of recognizing its new polycentric nature, which includes roles for non-state and subnational actors in climate change mitigation and in leadership for climate action. Yet, the literature is missing a normative cartography—that is, a mapping of the distribution of moral duties in the real world—which is tailored to a context of polycentric governance. This paper answers the question: how can moral duties be distributed in a context of polycentric climate governance such as to diminish the problem of non-compliance? This implies the following question: do duties change in situations of non-compliance in a context of polycentric governance, and if so how? Acknowledging polycentric governance is the key to an effective distribution of moral duties, as it allows for a more accurate mapping of non-state and subnational actors' duties in leading the charge against climate change. Correspondingly, a normative cartography fitted to this context will be instrumental in showing how morally informed climate governance can diminish the problem of non-compliance. This paper focusses on the distribution of moral duties in a context of polycentric governance as a contributing factor to inducing agents to act according to the collective goal. It argues that a more fine-grained distribution of climate duties, tailored to polycentric climate governance, contributes to addressing the problem of non-compliance. [ABSTRACT FROM AUTHOR]

de Freitas et al. (2015) (henceforth dFDB) report a trend of 0.28 °C per century over the period 1909-2009 for New Zealand land surface temperatures, from their reanalysis of a composite of seven long-term records. This is much lower than the warming trend of about 0.9 °C per century reported previously by other researchers and much smaller than trends estimated from independent sea surface temperature data from the surrounding region. We show these differences result primarily from the way inhomogeneities in temperature time series at individual stations due to site or instrument changes are identified and adjusted for in the dFDB paper. The adjustments reported in that paper are based on a method designed by one of us (Salinger), but use only a short (1-2-year) overlap period with comparison stations and consider only inhomogeneities in monthly mean (rather than monthly maximum and minimum) temperatures. This leads to underestimates of the statistical significance of individual temperature discontinuities and hence rejection of many valid adjustments. Since there was a systematic tendency for the seven-station sites to be relocated to colder locations as the early half of the twentieth century progressed, this rejection of valid adjustments produces an artificially low rate of warming. We therefore disagree with the trend calculations in the dFDB paper and consider there is no reason to reject the previous estimates of around 0.9 °C warming per century. [ABSTRACT FROM AUTHOR]

Climate change and environmental hazards affect the entire world, but their interactions with—and consequences on—human migration are unevenly distributed geographically. Research on climate and migration have their own geographies which do not necessarily coincide. This paper critically confronts these two geographies by presenting the first detailed mapping of research in the field of environmentally induced migration. After a brief review of the geography of research on climate change, the paper presents an overview of nearly 50 years of case studies on the basis of CliMig, a bibliographic database of 1193 scientific papers and books on climate/environmental change and migration, among them 463 empirical case studies. We analyze the locations of these case studies, the academic affiliations of their researchers, and the origin of their funding. Mapping the locations of case studies worldwide points toward blind spots in the research and identifies “overstudied” areas. We describe the methodologies used in the studies and present a typology of environmental hazards. Our results show that research on environmental migration is mainly done in countries of the Global South, whereas climate science research in general is focused on countries of the Global North. We contend that the peculiar geography of environmental migration cannot be explained solely by the uneven vulnerability of southern populations to the environment. It must also be understood through the lens of post-colonial and securitization studies as the result of a framing of “environmental refugees” (and refugees in general) as an intrinsically “southern problem” and as a security risk for the North. This paper is an original contribution to the literature on the North-South divide in scientific research and will help to outline future directions of investigation. [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]

The article discusses a research about the use of tree rings in providing information about the amount of snow or rainfall. It references a study by Kerstin Treydte and colleagues published within the issue. They concluded that the latter part of the twentieth century was by far the wettest period, suggesting that humans may play a role in the climate of the world.

The article focuses on the research on the role of climate data in predicting malaria outbreaks in Botswana. Researchers was able to identify the malaria season which starts during the rainy season in the country. Their model could help them determine which areas will be hit hardest by the epidemic. This advance information could help curb the epidemics by implementing preventive action.

The Journal of Agricultural, Biological and Environment Statistics (JABES) special issue on the Climate and Earth System highlights recent statistical develops that aim to refine our understanding of this complex system. New methods are required to process the massive environmental data that often fuels climate analysis and to properly account for uncertainty in the results. This special issue proudly features eight papers that span a wide range of computational and methodological problems related to the climate and earth system. In this brief introduction, we identify common themes among the papers and point to areas of future research. [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]

This review paper addresses the challenging question of “how to” design and implement co-production of knowledge in climate science and other environmental and agricultural sciences. Based on a grounded theory review of nine (9) published case studies of transdisciplinary and collaborative research projects, the paper offers a set of common themes regarding specific components and processes for the design, implementation, and achievement of co-production of knowledge work, which represent the “Modus Operandi” of knowledge co-production. The analysis focuses on practical methodological guidance based on lessons from how different research teams have approached the challenges of complex collaborative research. We begin by identifying broad factors or actions that inhibit or facilitate the process, then highlight specific practices associated with co-production of knowledge and necessary competencies for undertaking co-production. We provide insights on issues such as the integration of social and professional cultures, gender and social equity, and power dynamics, and illustrate the different ways in which researchers have addressed these issues. By exploring the specific practices involved in knowledge co-production, this paper provides guidance to researchers on how to navigate different possibilities of the process of conducting transdisciplinary and co-production of knowledge research projects that best fit their research context, stakeholder needs, and research team capacities. [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]

Climate clubs have been suggested as a gateway to substantial reductions in global emissions. The club approach begins with a small number of enthusiastic countries. This paper asks under what conditions such clubs are likely to evolve into effective cooperation through side-payments to new members. The question is addressed through a range of formal thought experiments using numerical simulations. The model is calibrated using empirical data on countries' emissions, GDP, populations, and vulnerabilities. It is simple and stylized, but allows for complex and dynamic interactions between actors. Basic equity considerations can be accommodated. The results indicate that side-payments' theoretical potential for facilitating effective clubs is large. One or two large emitters can initiate a club that grows to cover a substantial share of global emissions if the global benefit-cost ratio for mitigation is around 3 or larger. The size of stable clubs is larger if new members contribute to making side-payments, and somewhat lower if equity considerations constrain the set of possible transfers. Side-payments' effect is enabled by the large asymmetries between countries. Total side-payment flows range from tens to hundreds of billions of US dollars annually. [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 develops a climate-economy model with uncertainty, irreversibility, and active learning. Whereas previous papers assume learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from investment in monitoring, specifically in improved observations of the global mean temperature. We find that the decision maker invests a significant amount of money in climate research, far more than the current level, in order to increase the rate of learning about climate change. This helps the decision maker make improved decisions. The level of uncertainty decreases more rapidly in the active learning model than in the passive learning model with only temperature observations. As the uncertainty about climate change is smaller, active learning reduces the optimal carbon tax. The greater the risk, the larger is the effect of learning. The method proposed here is applicable to any dynamic control problem where the quality of monitoring is a choice variable, for instance, the precision at which we observe GDP, unemployment, or the quality of education. [ABSTRACT FROM AUTHOR]

This paper discusses ethical issues surrounding Integrated Assessment Models (IAMs) of the economic effects of climate change, and how climate economists acting as policy advisors ought to represent the uncertain possibility of catastrophe. Some climate economists, especially Martin Weitzman, have argued for a precautionary approach where avoiding catastrophe should structure climate economists' welfare analysis. This paper details ethical arguments that justify this approach, showing how Weitzman's "fat tail" probabilities of climate catastrophe pose ethical problems for widely used IAMs. The main claim is that economists who ignore or downplay catastrophic risks in their representations of uncertainty likely fall afoul of ethical constraints on scientists acting as policy advisors. Such scientists have duties to honestly articulate uncertainties and manage (some) inductive risks, or the risks of being wrong in different ways. [ABSTRACT FROM AUTHOR]

This paper explores the traditional indicators that small-scale farmers in Gaza province in southern Mozambique use to predict drought events on their rain-fed farms. It analyzes the contextual situation regarding the accuracy and reliability of the traditional prediction methods under the current weather and conditions of climate uncertainty and variability, and the opportunities that their prediction methods can bring to reduce their current and future exposure and vulnerabilities to drought. Farmers use a total of 11 traditional environmental indicators to predict drought, either individually or combined, as required to increase their prediction certainty. However, the farmers perceive that current unpredictability, variability, and changes in weather and climate have negatively affected the interpretation, accuracy, and reliability of most of their prediction indicators, and thus their farming activities and their ability to predict and respond to drought. This, associated with the reduced number of elders in the community, is causing a decline in the diversity, and complexity of interpretation of indicators. Nonetheless, these difficulties have not impeded farmers from continuing to use their preferred prediction methods, as on some occasions they continue to be useful for their farming-related decisions and are also the main, or sometimes only, source of forecast. Considering the role these methods play in farmers' activities, and the limited access to meteorological forecasts in most rural areas of Mozambique, and the fact that the weather and climate is expected to continually change, this paper concludes that it is important to enhance the use of traditional prediction methods. However, the increase of the accuracy and reliability, and continued existence of the methods depends on the farmers' own abilities to enhance, preserve, and validate them by tailoring the traditional methods used to work with the new environmental, weather, and climatic conditions, or through the development of new methods. [ABSTRACT FROM AUTHOR]

This paper introduces three quantitative indicators to conduct research for characterizing Northeast China cold vortex persistence activity: cold vortex persistence, generalized "cold vortex," and cold vortex precipitation. As discussed in the first part of paper, a hindcast is performed by multiple regressions using Northeast China precipitation from 2012 to 2014 combination with the previous winter 144 air-sea system factors. The results show that the mentioned three cold vortex index series can reflect the spatial and temporal distributions of observational precipitation in 2012–2014 and obtain results. The cold vortex factors are then added to the Forecast System on Dynamical and Analogy Skills (FODAS) to carry out dynamic statistical hindcast of precipitation in Northeast China from 2003 to 2012. Based on the characteristics and significance of each index, precipitation hindcast is carried out for Northeast China in May, June, July, August, May–June, and July–August. It turns out that the Northeast Cold Vortex Index Series, as defined in this paper, can make positive corrections to the FODAS forecast system, and most of the index correction results are higher than the system's own correction value. This study provides quantitative index products and supplies a solid technical foundation and support for monthly precipitation forecast in Northeast China. [ABSTRACT FROM AUTHOR]

In climate-economic modelling, agent-based models are still an exception. Although numerous authors have discussed the usefulness of the approach, only a few models exist. The paper proposes an update to a multi-agent climate-economic model, namely the “battle of perspectives” (Janssen, 1996; Janssen and de Vries 1998). The approach of the paper is twofold. First, the reimplementation of the model follows the “model to model” concept. Supporters of the approach argue that replication is a useful way to check a model’s accuracy and robustness. Second, updating a model with current data and new scientific evidence is a robustness check in itself. The long-term validity and usefulness of a model depends on the variability of the data on which it is based, as well as on the model’s sensitivity to data changes. By offering this update, the paper contributes to the development of agent-based models in climate-economics. Acknowledging evolutionary processes in climate-policy represents a useful complement to intertemporal cost-benefit analyses, the latter of which derive optimal protection paths but are not able to explain why people do not follow them. Since the replication and update succeeded, the paper recommends using the model as a basis for further analysis. [ABSTRACT FROM AUTHOR]

Arthur G. Tansley never accepted Frederic E. Clements' view that succession is a developmental process whose final stage, the climax formation, is determined primarily by regional climate and that all other types of vegetation are some kind of successional stage or arrested successional stage. Tansley was convinced that in a given region a variety of environmental factors could produce different kinds of climax formations. At the heart of their dispute was Clements' organicist view of succession, i.e., the formation was a complex organism with an ontogeny and phylogeny. As early as 1905, Tansley offered an alternative to Clements' complex organism, the quasi-organism, but Clements in private and public rejected this compromise. Tansley and other plant ecologists continued to criticize Clements' theories for the next 20 years, but with no impact on Clements. John Phillips, a South African plant ecologist who was a follower of Clements, published a series of papers in 1934 and 1935 defending Clementsian ecology. These papers were triggered by the publication of a letter by another ecologist working in Africa who claimed that there was a strong correlation between soils and various kinds of climax vegetation, which was contrary to what was predicted by Phillips and Clements. In 1935, Tansley published an attack on Phillips and Clements and their developmental theory of succession. In it, he proposed the concept of the ecosystem as a way to get around Clements' monoclimax theory by making the physical environment (e.g., soil chemistry, soil texture, soil moisture) as important a factor as climate, plants and other organisms in determining the composition and characteristics of ecological entities, i.e., ecosystems. Tansley's ecosystem concept quickly replaced Clements' monoclimax theory as a dominant paradigm in ecology. [ABSTRACT FROM AUTHOR]

Tourism climatology past and present: A review of the role of the ISB commission on climate, tourism and recreation. While in the emergent phase of tourism climatology, a majority of studies were either strong in climatology or tourism leading to a rather one-dimensional representation of this complex system and its interrelationships. Special issue papers Among the most-studied research areas in the tourism climatology literature is snow sports tourism, which is understandable given its reliance on climatic resources (i.e., low temperatures, snowfall) and thereby high vulnerability to climate change (Steiger et al. [28]). Moving forward From inception, the CCTR has sought to bring together scientists and tourism experts to review the current state of knowledge of tourism climatology, while exploring future areas of inquiry by supporting multi- and interdisciplinary collaborations. [Extracted from the article]

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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