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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]

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]

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]

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]

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]

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]

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]

Tianshan Mountains are the headwater regions for the central Asia rivers, providing water resources for ecological protection and economic development in semiarid regions. Due to scarce observations, the hydroclimatic characteristics of the Tianshan Mountains Precipitation (TMP) measured over highland (>1500 m) regions remain to be revealed. Here, we show the TMP belongs to a monsoon-like climate regime, with a distinct annual range and a high ratio of summer-to-yearly rainfall, and exhibits six abrupt changes, dividing the annual cycle into six precipitation sub-seasons. Over the past 60 years, the yearly TMP has significantly increased by 17.3%, with a dramatic increase in winter (135.7%). The TMP displays a significant 40-day climatological intra-seasonal oscillation (CISO) in summer. The TMP CISO's wet phase results from the confrontation of the eastward propagating mid-tropospheric Balkhash Lake Low and the southward migrating Mongolian High. The sudden changes in the two climatological circulation systems trigger TMP's changes, shaping the 40-day CISO. Emerging scientific issues are also discussed. [ABSTRACT FROM AUTHOR]

Global warming is a current problem that needs to be addressed by collaborating with researchers from different disciplines and expertise. There is a concern about training the next generation of scientists to holistically address climate change. One way to address this concern is gathering researchers to promote collaboration, as a critical aspect of the scientific perspective. This study focused on the interactions among 119 early career researchers from different disciplines and countries attending the São Paulo School of Advanced Science on Climate Change 2017 in Brazil. The aim of this research case study was to identify factors and social dynamics influencing the effective generation of collaborative networks. First, social perceptions were assessed to expose factors influencing the open exchange of knowledge and dynamics among the new researchers. Second, the occurrence of interdisciplinary and intercultural interactions was evaluated using social network analysis. Using the qualitative and quantitative outcomes, perceived indicators related to barriers (e.g., language and background) and drivers (e.g., gender and age) were linked to the structure of two social networks analyzed: workplace/studyplace, and professions. Social network analysis showed that although the participants aggregated challenging the goal of collaborative networks, they actively sought interdisciplinary approaches. Based on detected barriers, future events organizers searching for an interdisciplinary climate change approach are encouraged to actively overcome these limitations by taking into account the culture, beliefs, and conceptualization aspects at a group level without overlooking individual preferences. [ABSTRACT FROM AUTHOR]