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This study highlights the value of grey literature in climate change research in the Philippines, emphasizing its essential role in understanding the issue within the country's unique socioeconomic and natural environment. A bibliometric analysis of publications in local journals reveals that various types and formats of grey literature are not merely supplementary but essential for communicating climate change, its effects, and adaptation strategies. Despite the prevalence of major publishers, a substantial amount of research in the country continues to be disseminated through local journals. However, these journals face significant challenges, including limited coverage in major databases and concerns about quality, which hinder their global recognition and impact. The findings recommend that the Commission on Higher Education (CHED) enhance the capacity of higher education institutions (HEIs) to improve the quality and credibility of local journals. This improvement would increase their visibility and reach, ensuring that the comprehensive body of Filipino research is preserved, recognized, and utilized in the global discourse on climate change. Consequently, this will foster a more inclusive and informed approach to understanding and addressing this global challenge. [ABSTRACT FROM AUTHOR]

• The segmented exponential function is used to take into account PWV variation at different elevations. • Reducing model coefficient storage using a sliding window algorithm. • In ERA5 and RS PWV profiles, CPWV-H model with higher stability and accuracy of vertical adjustment. Precipitable Water Vapor (PWV) is vital in climate research, monitoring in troposphere stability and weather disasters. This study investigates the detailed vertical variation of PWV in China, utilizing data from the 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF). A PWV segmented vertical adjustment grid model (CPWV-H) was developed based on a sliding window algorithm, with a spatial resolution of 0.5° × 0.5° and a temporal resolution of one day. The performance of the CPWV-H model was evaluated using multi-source PWV data and compared with the conventional empirical model (EPWV-H) and the recently released C-PWVC1 model. Results show that the CPWV-H model has superior accuracy and stability. Using ERA5 PWV profiles as a reference, the CPWV-H model has a mean Bias of −0.45 mm and a mean RMSE of 1.24 mm, improving by 21.52 % over the EPWV-H model and 31.11 % over the C-PWVC1 model. With radiosonde profiles as a reference, it has a mean Bias of −0.10 mm and a mean RMSE of 2.86 mm, improving by 7.14 % and 13.86 % over the EPWV-H and C-PWVC1 models, respectively. Consequently, the CPWV-H model exhibits enhanced stability and vertical adjustment accuracy in China, offering significant utility for the fusion of multi-source water vapor data, comparative analysis, and climate research. [ABSTRACT FROM AUTHOR]

The most disastrous heatwaves are very extreme events with return periods of hundreds of years, but traditionally, climate research has focussed on moderate extreme events occurring every couple of years or even several times within a year. Here, we use three Earth System Model large ensembles to assess whether very extreme heat events respond differently to global warming than moderate extreme events. We find that the warming signal of very extreme heat can be amplified or dampened substantially compared to moderate extremes. This modulation is detectable already in mid-century projections. In the mid-latitudes, it can be explained by changes in event soil moisture-temperature coupling during the hottest day of the year. The changes depend on the interplay of present soil moisture and coupling during heat events as well as projected precipitation changes. This mechanism is robust across models, albeit with large spatial uncertainties. Our findings are highly relevant for climate risk assessments and adaptation planning. Climatic changes in extreme events are well studied, but research has so far focused on moderate extremes. The authors show that changes in very extreme heat events can be, depending on the region, much stronger or weaker than changes in moderate extremes. [ABSTRACT FROM AUTHOR]

Climate change is a significant threat to ecosystems and communities, and challenges global sustainability; therefore, more research on climate change adaptations should be conducted. This scientometric study presents databases on global trends in academic research, specific contributions, and thematic evolution of environmental adaptations to climate change. Using the Scopus database of publications, this study focuses on publications between 2009 (January) and 2024 (August), with an emphasis on the interpretation of primary research interests, authors, contributions from countries, and collaborative networks. According to the findings, there has been a shift in focus to more adaptive management in agricultural and biological diversity practices, with more emphasis on the integration of ecological, technological, and social science disciplines. This work also reveals that countries have gaps in regional research, especially in developing countries, and highlights the need to work collectively across the globe. Through citation analysis, thematic evolution, and future research recommendations, this study enriches the knowledge of the line of research on environmental adaptation and underlines its significance in addressing climate change effects. [ABSTRACT FROM AUTHOR]

This study explores a prediction system for global horizontal irradiance and cloudiness in a humid subtropical terrestrial region. This system consists of regional simulations performed with the Weather Research and Forecasting model using the initial and boundary conditions from the Global Forecast System. The predictions show significant biases for the variable of interest, with notable variations within the daily and annual cycles. This study also finds significant biases in cloud incidence and clarity index predictions, with relevant diurnal and seasonal variations. During austral summer, multiplying the relative humidity of initial and boundary conditions by a fixed factor improves the forecasts of global horizontal irradiance and cloud incidence for the central hours of the day and the afternoon. During austral winter, an empirical correction of the clarity index obtained from the simulation's outputs also shows the potential to improve the forecasts' biases. This work proposes a hypothesis about the causes of the forecast biases. [ABSTRACT FROM AUTHOR]

This paper explores the personal experiences of Sandra Kaire as a postdoctoral researcher who worked with Margaret Somerville in relation to methodological approaches. The ideas were initially developed in consultation when Sandra worked together with Margaret at Western Sydney University, Australia. The paper explores Sandra's personal commitments and intimate engagement with young climate activists, and the development of visual, place-based and posthuman methodological approaches in relation to Sandra's postdoctoral research with young climate activists in three European counties. It includes photographs of their activist environments, observations, and transcripts of in-depth interviews. The paper reveals the significance of climate activism experiences of young people to their educational concerns, and the relationships of the locations of their activism to experiences of becoming more than itself by adding new ways of thinking about human and more-than-human relation. [ABSTRACT FROM AUTHOR]

Warm fronts often trigger significant weather changes, which also play a role in many extreme weather incidents. Therefore, it is crucial to understand the location and characteristics of warm fronts to accurately forecast weather changes. However, warm fronts are more difficult to identify than cold fronts on average, since the gradients can be weaker and shallower. This paper proposes a new objective method for identifying warm fronts in the Eurasia region using ERA‐5 hourly reanalysis data. The method uses the appropriate thermal front parameter and warm advection threshold to identify the potential warm frontal zone, then determines the corresponding warm boundary according to the predominant wind direction within the frontal zone, and finally locates the warm front line along the warm boundary. In various weather processes, the location and shape of the objective warm front correspond well with the distribution of weather systems and meteorological elements, indicating the effectiveness of the method. Meanwhile, the high agreement between objective and manual warm fronts further supports the reliability of the method. Furthermore, this method is applied to the long‐term datasets covering Eurasia, enabling an exploration of the climatological characteristics of warm fronts in the region. The study reveals distinct seasonal patterns in warm front frequency, with the highest frequency occurring during winter and the lowest during summer. Warm fronts are notably active in Europe, the Siberian Plain, the Northeast China Plain extending to the Western Pacific during winter, spring, and autumn. The dataset of warm fronts produced by this method proves valuable for climate change research. [ABSTRACT FROM AUTHOR]

Streamflow records are biased toward large streams and rivers, yet small headwater streams are often the focus of ecological research in response to climate change. Conventional flow measurement instruments such as acoustic Doppler velocimeters (ADVs) do not perform well during low‐flow conditions in small streams, truncating the development of rating curves during critical baseflow conditions dominated by groundwater inflow. We revisited an instantaneous solute tracer injection method as an alternative to ADVs based on paired measurements to compare their precision, efficiency, and feasibility within headwater streams across a range of flow conditions. We show that the precision of discharge measurements using salt dilution by slug injection and ADV methods were comparable overall, but salt dilution was more precise during the lowest flows and required less time to implement. Often, headwater streams were at or below the depth threshold where ADV measurements could even be attempted and transects were complicated by coarse bed material and cobbles. We discuss the methodological benefits and limitations of salt dilution by slug injection and conclude that the method could facilitate a proliferation of streamflow observation across headwater stream networks that are highly undersampled compared to larger streams. [ABSTRACT FROM AUTHOR]

Tree‐ring data are pivotal for decoding the age and growth patterns of trees, reflecting the impact of environmental factors over time. Addressing the significant shortcomings of traditional, labour‐intensive and resource‐demanding methods, we propose an innovative automated technique that utilizes panchromatic images and deep learning for measuring tree rings. The method utilizes convolutional neural networks to enhance image quality, precisely delineate tree rings through segmentation and perform ring counting and width calculation in the post‐processing stage. We compiled an extensive data set from diverse sources, including Beijing Forestry University and the Summer Palace, to train our algorithm. The performance of our method was validated empirically, demonstrating its potential to transform tree‐ring analysis and provide deeper insights into ecological and climatological research. Summary statement: We have developed a framework utilizing deep learning and panchromatic sensors for the automated measurement of tree rings. This framework achieves high precision in both ring detection and width measurement, offering an efficient alternative to the traditionally time‐consuming and labour‐intensive techniques of dendrochronology. [ABSTRACT FROM AUTHOR]

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This article develops the argument that the interplay between emotions and cognitive biases influences corporate decision-making on climate change adaptation. Our theoretical analysis examines how emotions can change the effect of cognitive biases on adaptation decisions by influencing how firms select, access, and process complex and uncertain climatic information. We draw on research on climate adaptation, social psychology, and managerial cognition and focus on three forms of bias: availability heuristic, framing, and anchoring. We explain how each bias shapes the decision-making process on adaptation and theorize how emotions of different valence and arousal affect this process. We shed light on the underlying mechanisms that explain how emotions influence the effect of cognitive biases as a source of inaction on adaptation in firms. Our analysis provides a new perspective on how firms approach the strategic decision to adapt to climate change by considering both cognitive and emotional aspects. [ABSTRACT FROM AUTHOR]

The earliest and often most significant impacts of climate change in the American West is on water resources. For communities reliant on irrigated agriculture, ensuring water security in the face of climate change can be critical to the preservation of local economies and understanding water management practices can support climate resilience. Research techniques such as behavioral games can be used to study how incentives and interventions influence community behaviors toward water conservation practices. This work explores the development and evaluation of a digital behavioral economic game using data from the San Luis Valley in order to improve understanding of how groundwater is consumed and conserved within agricultural communities in Colorado beyond traditional study techniques. In 2023, stakeholders were recruited to play and validate the game as a tool for understanding groundwater conservation practices and discuss the advantages of this study mechanism compared to standard methods such as surveys that do not account for dynamic and interactive decision-making. Results showed that financial incentives significantly influenced participants' crop choices and water use patterns, while demonstrating the impact of data transparency and revealing the development of collaborative behaviors. Qualitative assessments provided insights into real-world challenges, including state pressure to restore aquifer levels and threats to the local economy's reliance on irrigated agriculture. Discussions emphasized the need for improved water conservation education and more effective subsidy approaches for agricultural practices. The study tool will help policy makers explore and pilot new institutional innovations to prevent overexploitation of natural renewable resources and can be used to inform and empower communities to actively engage in collaborative problem-solving processes leading to improved community water conservation efforts. [ABSTRACT FROM AUTHOR]

Understanding the relationships between internal variability and forced climate feedbacks is key for using observations to constrain future climate change. Here we probe and interpret the differences in these relationships between the climate change projections provided by the CMIP5 and CMIP6 experiment ensembles. We find that internal variability feedbacks better predict forced feedbacks in CMIP6 relative to CMIP5 by over 50%, and that the increased predictability derives primarily from the slow (>20 years) response to climate change. A key novel result is that the increased predictability is consistent with the higher resemblance between the patterns of internal and forced temperature changes in CMIP6, which suggests temperature pattern effects play a key role in predicting forced climate feedbacks. Despite the increased predictability, emergent constraints provided by observed internal variability are weak and largely unchanged from CMIP5 to CMIP6 due to the shortness of the observational record. Plain Language Summary: A key goal in climate change research is to use observed, internal climate feedbacks to constrain the forced feedbacks that govern climate change. Here the authors explore the differences in the relationships between internal and forced climate feedbacks in simulations run under the auspices of the two recent IPCC reports: The CMIP5 and CMIP6 simulations. They find notable increases in the relationships between internal and forced feedbacks between CMIP5 and CMIP6, and attribute these increases at least partially to the patterns of temperature variability associated with internal climate variability and forced climate change. However, they argue that the increases do not lead to improvements in our ability to constrain future climate change based on observations due to the uncertainty in the observed, internal climate feedbacks. Key Points: Internal variability feedbacks better predict forced feedbacks in CMIP6 relative to CMIP5 by over 50%The improved prediction derives in part from the greater similarity between the patterns of internal and forced temperature changes in CMIP6The improved prediction does not significantly improve the emergent constraint associated with internal variability feedbacks [ABSTRACT FROM AUTHOR]

Urban areas are recognized as critical zones for climate research due to the high number of people living in these areas and their significant impacts on local and regional climates. However, understanding urban boundary layer processes remains a challenge, as existing mesoscale models cannot resolve their fine-scale features and dynamics, while microscale fluid dynamics simulations remain computationally expensive or unfeasible for the full extent of the urban atmosphere. To address this gap, we present PALM-SLUrb, a single-layer urban canopy model for the PALM model system, offering a computationally efficient and physics-based model to represent urban surfaces on non-building-resolving grids. Together with the model description, we present sensitivity tests and a model comparison against grid-resolved urban canopies to demonstrate the model's performance. The results demonstrate the model's ability to extend the representation of key urban–atmosphere interactions in PALM into coarser grid resolutions on the order of 10 metres. By bridging the gap between computational efficiency and physical detail, PALM-SLUrb broadens PALM's capabilities in advancing urban climate research. [ABSTRACT FROM AUTHOR]

This paper provides an updated assessment of the "International Research Institute for Climate and Society's (IRI) El Niño Southern Oscillation (ENSO) Predictions Plume". We evaluate 253 real-time forecasts of the Niño 3.4 index issued from February 2002 to February 2023 and examine multimodal means of dynamical (DYN) and statistical (STAT) models separately. Forecast skill diminishes as lead time increases in both DYN and STAT forecasts, with peak accuracy occurring post-northern hemisphere spring predictability barrier and preceding seasons. The DYN forecasts outperform STAT forecasts with a pronounced advantage in forecasts initiated from late boreal winter through spring. The analysis uncovers an asymmetry in predicting the onset of cold and warm ENSO episodes, with warm episode onsets being better forecasted than cold onsets in both DYN and STAT models. The DYN forecasts are found to be valuable for predicting warm and cold ENSO episode onsets at least several months in advance, while STAT forecasts are less informative about ENSO phase transitions. The results indicate that predicting ENSO onset is challenging and that the ability to do so is both model- and event-dependent. [ABSTRACT FROM AUTHOR]

The Atmospheric Environment Monitoring Satellite (AEMS), launched by China in 2022, was equipped with active remote sensing lidar for carbon monitoring. It adopts the Integrated Path Differential Absorption (IPDA) technology to monitor global CO2 column concentration (XCO2). The calculation of cloud top XCO2 requires cloud height data. A comparison between SRTM global elevation data and 1,572 nm channel elevation data reveals a coefficient of determination (R2) of 0.998, with an average deviation of 1.24 m. The cloud top XCO2 observations are consistent with the OCO‐2 and CarbonTracker trends. The ocean carbon uptake rate, assessed by the difference in CO2 concentration between cloud top and sea surface, is −0.319 mmol/m2/h, which is in good agreement with the associated carbon flux data. This demonstrates the great potential of IPDA lidar for remote sensing of cloud top CO2 and quantifying ocean carbon uptake. Plain Language Summary: For global greenhouse gas monitoring, passive remote sensing technology has consistently struggled to balance the reliability and usability of monitoring data in cloudy regions. The AEMS employs 1,572 nm IPDA lidar technology for active remote sensing of global XCO2, enabling effective processing and utilization of cloud echo data. In this study, we focused on the concentrations of CO2 columns using cloud top echoes and performed a preliminary comparison of cloud top XCO2 results with related data products from the passive satellite OCO‐2 and CarbonTracker. By quantifying the difference in CO2 concentration between two altitude layers above the sea surface, we assessed ocean carbon absorption capacity, and the results demonstrated high reliability. This work highlights the significant advantages of spaceborne IPDA lidar in global CO2 measurement, cloud echo data processing, and ocean carbon flux assessment, providing valuable data support for climate change research. Key Points: Inverting cloud top XCO2 using lidar reflection signals above the cloud enhances the utilization of satellite observation dataBased on passive satellite and model data, the accuracy of atmospheric environment monitoring satellite data inversion is demonstratedFirst use of active remote sensing satellites for assessment of marine carbon uptake [ABSTRACT FROM AUTHOR]

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs' skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies. [ABSTRACT FROM AUTHOR]

Health and climate drivers and solutions in Africa co-exist within the same social context. Primary healthcare systems are increasingly becoming impacted by climate change. Health and climate change communities still work in isolation in addition to limited multidisciplinary and integrated research on health and climate change to inform programming, and policy and unlock climate finance. Holistic and transdisciplinary approaches that make use of resources and inputs from different sectors are key to guaranteeing effective responses to the health impacts of climate change. This paper aims to understand the landscape of climate and health (C&H) research, policy capacity building, and stakeholders hence identifying models of collaboration that could be scaled up to strengthen transdisciplinary C&H research and action. A mixed method approach was used in this study that involved the combination of consultative webinars (n = 411) online surveys (n = 198), Focused Group Discussions (n = 123), and Key informant interviews (n = 25) used on targeted stakeholders. In addition, a scoping review of policy academic documents, and databases was done. It was evident that engaging partners from diverse sectors is crucial for the broad range of perspectives, interests, skills, levels of knowledge, and resources. Stakeholder engagement in C&H initiatives is increasing and yet unevenly distributed in Africa due to funding patterns and other structural patterns. Existing C&H research predominantly focuses on climate impacts on diseases, particularly vector-borne and infectious illnesses. While crucial, this disease-centric approach limits the provision of integrated solutions for complex African socio-economic contexts. In addition, Dedicated C&H capacity-building efforts are emerging, but they mainly focus on research and policy, overlooking critical areas like leadership, investment skills, and impact assessment. Health governance and climate change should be addressed through a holistic approach that is transdisciplinary and multisectoral and through the transborder framework. The approach will need innovative thinking in socio-economies, policies, ecologies, and geographies with emphasis centered on the most vulnerable groups. [ABSTRACT FROM AUTHOR]

Water Infrastructure (WI), incorporating water supply, wastewater, and stormwater systems, is vulnerable to climate change impacts that can disrupt their functionality; thus, WI needs to be adapted to climate change. In 2021, the European Commission (EC) released the technical guidelines on "Climate-proofing Infrastructure" that include mitigation and adaptation strategies; these guidelines, and the relevant guides that followed, focus mainly on climate change aspects without examining sufficiently the engineering features of WI that are described mainly in the relevant hydro-environmental research; this research is vast and includes various terminologies and methods for all aspects of climate change adaptation. The adaptation procedure of WI to climate change is improved when this research is known to guidelines' developers. In the present work, to facilitate this knowledge transfer, we typologize the hydro-environmental research via its classification into five categories that are based on the EC guidelines and then perform a literature review that we present as follows: firstly, we introduce and typologize the climate hazards for WI systems and identify the most common of them in the Mediterranean region that we classify into seven groups; then, we classify the hydro-environmental research into five categories based on the EC guidelines, present the main aspects for each of these categories, discuss the future research; and finally, we summarize the conclusions. [ABSTRACT FROM AUTHOR]

This commentary reflects on the contributions that human geography has made to climate change research, as well as future directions for human geographic work on climate change. I suggest that one of the key achievements of the field is to render climate change a boundary object: an entity whose interpretive flexibility allows it to become a shared object of knowledge that, in principle, facilitates collaboration across disparate communities of practice. However, the ways in which that contribution has been made, and made possible, have effects that ripple elsewhere. While the subfield of political ecology and its historiographical methods have played vital roles in laying the intellectual foundations for human geographic work on climate change, and thus its ability to construct climate change as a boundary object in the first place, I suggest that they and other neo‐Marxian approaches to the study of the (climate‐changing) environment tend to diminish human geographic engagement with the future as an object of scholarly inquiry. To address that limitation and help push climate‐changed geographies in novel directions, I suggest that climate‐changed futures be engaged problematically. I then conclude with a discussion of key empirical domains—chiefly climate reparations and climate adaptation—where such problem‐oriented work may be productively applied. This commentary explores one of human geography's key contributions to climate change knowledge: that it has helped make climate change political. However, the ways that human geographers have made climate change political often rely on historiographic methods, which can bound geographic engagement with the future as an object of inquiry in its own right. The commentary then offers ways to open up the future in geographic work on climate change, specifically through problem‐oriented and reparative critique. [ABSTRACT FROM AUTHOR]

Geography and geographers have a long and influential history of research on climate change, with a significant impact on scholarship, international assessments and public policy. In this commentary, I focus on geographic work on climate vulnerability, arguing that geographers have been fundamental to our understanding of the impacts of climate variability and climate hazards and to the assessments of Working Group 2 of the Intergovernmental Panel on Climate Change (IPCC). Geographic work on climate vulnerability is traced to pioneering work in hazards and cultural ecology and to the rise of critical perspectives on the political economy and political ecology of hazards and then climate change. I discuss the role of geographers in the IPCC from 1990 to present and the importance of concepts originating in geography such as vulnerability mapping, double exposure, borderless climate risks, discourses, intersectionality, climate‐resilient development, labour vulnerabilities and maladaptation. Geography and geographers have a long and influential history of research on climate change, with a significant impact on scholarship, international assessments and public policy. In this paper, I focus on geographic work on climate vulnerability, arguing that geographers have been fundamental to our understanding of the impacts of climate variability and climate hazards, and to the assessments of Working Group 2 of the Intergovernmental Panel on Climate Change (IPCC). I analyse how the study of vulnerability has tracked broader theoretical and research trends in geography, most notably the emergence of cultural ecology and hazards, political economy, GIS and remote sensing, and critical perspectives such as political ecology, feminism and climate justice. [ABSTRACT FROM AUTHOR]

Satellite-retrieved sea-surface skin temperature (SSTskin) is essential for many Near-Real-Time studies. This study aimed to assess the potential to improve the accuracy of satellite-based SSTskin retrieval in the Caribbean region by using atmospheric correction algorithms based on four readily available machine learning (ML) approaches: eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and the Artificial Neural Network (ANN). The ML models were trained on an extensive dataset comprising in situ SST measurements and atmospheric state parameters obtained from satellite products, reanalyzed datasets, research cruises, surface moorings, and drifting buoys. The benefits and shortcomings of various ML methods were assessed through comparisons with withheld in situ measurements. The results demonstrate that the ML-based algorithms achieve promising accuracy, with mean biases within 0.07 K when compared with the buoy data and ranging from −0.107 K to 0.179 K relative to the ship-derived SSTskin data. Notably, both XGBoost and RF stand out for their superior correlation and efficacy in the statistical results of validation. The improved SSTskin derived using the ML-based algorithms could enhance our understanding of vital oceanic and atmospheric characteristics and have the potential to reduce uncertainty in oceanographic, meteorological, and climate research. [ABSTRACT FROM AUTHOR]

In May and June 2024, a die‐off of Mexican mantled howler monkeys (Alouatta palliata mexicana) occurred in southern Mexico. This commentary documents the event, attributing it to extreme heatwaves, drought, wildfires, and habitat impoverishment. Despite their reported resilience to habitat disturbances, mantled howler monkey mortality rate in some areas reached 31%. Key evidence points to heatstroke as the primary cause of death, exacerbated by limited hydration and reduced dietary diversity in disturbed habitats. Immediate responses included community‐led rescues (e.g., hydrating the monkeys), coordination of rescue activities by nongovernmental organizations (NGOs) (e.g., managing donations), involvement of scientists (e.g., monitoring of primate populations), and assistance from government officials (e.g., providing legal support for animal management). This event underscores the urgency of developing action plans to prevent and attend future crises. Among other actions, we highlight (i) establishing primate care infrastructure with medical and rehabilitation centers; (ii) developing protocols and training programs to ensure rapid crisis response; (iii) fostering collaboration among government, NGOs, and academic institutions for effective crisis management; and (iv) developing targeted research on climate change impacts, predictive models, and long‐term health monitoring. We emphasize the critical need for coordinated conservation efforts to protect wild primates and maintain natural ecosystem resilience in the face of escalating climate challenges. Summary: In May and June 2024, over 300 howler monkeys died in southern Mexico.Key evidence points to heatstroke as the primary cause of death, exacerbated by limited hydration and reduced dietary diversity in disturbed habitats.This event highlights the critical need for coordinated conservation efforts to protect wild primates in the face of escalating climate challenges. [ABSTRACT FROM AUTHOR]

Lake temperature forecasting is crucial for understanding and mitigating climate change impacts on aquatic ecosystems. The meteorological time series data and their relationship have a high degree of complexity and uncertainty, making it difficult to predict lake temperatures. In this study, we propose a novel approach, Probabilistic Quantile Multiple Fourier Feature Network (QMFFNet), for accurate lake temperature prediction in Qinghai Lake. Utilizing only time series data, our model offers practical and efficient forecasting without the need for additional variables. Our approach integrates quantile loss instead of L2-Norm, enabling probabilistic temperature forecasts as probability distributions. This unique feature quantifies uncertainty, aiding decision-making and risk assessment. Extensive experiments demonstrate the method's superiority over conventional models, enhancing predictive accuracy and providing reliable uncertainty estimates. This makes our approach a powerful tool for climate research and ecological management in lake temperature forecasting. Innovations in probabilistic forecasting and uncertainty estimation contribute to better climate impact understanding and adaptation in Qinghai Lake and global aquatic systems. [ABSTRACT FROM AUTHOR]

The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years, it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging 2 years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring the original CERRA. Validation with in-situ observations further confirms the model's accuracy in approximating ground measurements. [ABSTRACT FROM AUTHOR]

This paper addresses how research and innovation (R&I) policy responds to global challenges, which demand fundamental transformations of societies and economies. The paper presents the case of a transnational, European funding call (SOLSTICE) for climate change research led by social sciences and humanities. Both the development of SOLSTICE and the research community's responses are analysed in relation to the transformative R&I policy literature, identifying potential future avenues for improving the transformative outcomes of this type of funding. The study concludes that unlocking the value of transformative R&I policy concepts and approaches depends on wide stakeholder engagement in the funding instrument design phase and an active and sustained focus on achieving societal outcomes during project implementation. In addition, barriers to the effectiveness of (European) transnational funding programmes are identified, particularly when policies target community-level socio-technical transitions to address global challenges. [ABSTRACT FROM AUTHOR]

There is no doubt that global climate change is happening and affecting life on Earth in a variety of ways. It can be seen on the extreme events of natural disasters, prolonged periods of drought, and increased summer and annual temperatures. While climate change affects every place on Earth, the Mediterranean region is considered a hot spot of climate change. Temperature is expected to increase further, precipitation, especially during summer months, is expected to decrease, and extreme rainfall events are projected to increase. These projected changes will affect both continental and insular environments, with small islands being particularly vulnerable due to the lack of space for species to move into more favorable conditions. As a result, these environments need to be studied, the changes quantified, and the consequences monitored. The current study focuses on the island of Fournoi in the central eastern part of the Aegean Sea. We employed data from a local meteorological station, which operates for a limited period, the Climate Research Unit TS data, and remote sensing thermal data to monitor the trends in aridity over a period of almost 40 years. The results show that summer temperature has increased significantly over the last 40 years, and this is confirmed by both meteorological and remote sensing data. At the same time, precipitation seems to remain stable. Despite the increased aridity imposed by the increased temperature and stable precipitation, vegetation seems not to be experiencing extreme stress. On the contrary, it seems to be following a positive trend over the study period. This observation is explained by the extreme resilience of the plant species of the study area and the fact that vegetation has been recovering over the last 50 years after a period of human overexploitation, and this recovery overcomes the stress imposed by increased aridity. [ABSTRACT FROM AUTHOR]

Urban Canopy Parameters (UCPs) are crucial for urban microclimate modeling; however, the scarce availability of precise UCP data in developing regions limits their application for urban climates. This study investigated the use of multi-platform remote sensing data viz. very high-resolution satellite (VHRS) optical stereo and Unmanned Aerial Vehicle (UAV) datasets for the computation of UCPs in high-density urban scenarios in India, with varied development characteristics. The results demonstrated high accuracy in terms of building height and footprint extraction from both datasets, key inputs for UCP computation. However, UCPs from UAV data have displayed relatively high accuracy for building footprints (86%), building height (RMSE ~ 0.05 m), and land use/land cover classification (90%). Performance evaluation of computed UCPs against a 3D reference geodatabase showed high prediction accuracy for most UCPs, with overall biases, mean absolute error, and root-mean-square error values significantly better than 1 m, with strong correlation (0.8–0.9). It was concluded that VHRS optical stereo and UAV datasets offer a secure, reliable, and accurate solution for UCP computation in urban areas, particularly in developing regions. These findings have significant implications for urban climate research and the sustainable development of rapidly urbanizing areas facing resource and policy constraints. [ABSTRACT FROM AUTHOR]

Estimation of reference evapotranspiration values is crucial in climatological and hydrological research, agricultural engineering, and irrigation design. The Penman-Monteith method, endorsed by the Food and Agriculture Organization (FAO) of the United Nations and numerous research studies, is widely regarded as the gold standard. However, its extensive data requirements limit its applicability in regions with sparse meteorological networks or limited measurement capabilities. The Hargreaves method, which requires only basic air temperature inputs, offers an alternative solution. The aims of this study were to calibrate the Hargreaves model for Central European climate conditions, considering altitudinal dependence, and to evaluate the temporal stability of the model parameters. In the first part of the research, we regionalized the Hargreaves coefficients using a curve-fitting method to ensure the best accuracy across 60 climatological stations in Slovakia. The regionalization of the Hargreaves coefficient improved accuracy by 10.1%, reducing the weighted absolute percentage error (WAPE) to 17.9%. However, our results showed that the accuracy of the modified Hargreaves model decreased with the increasing altitude of a climatological station. Incorporating altitude into the Hargreaves equation significantly improved model accuracy in stations at higher altitudes, providing a consistent level of accuracy across all climatological stations, regardless of their location and altitude. The results also indicated that the optimal model coefficient values change over time, showing a decreasing trend of –0.5 for the B coefficient and –0.1 for the C coefficient between the periods 1981–2000 and 2001–2020. Although regionalizing the Hargreaves model coefficients for local conditions can achieve good model performance, the model's accuracy is not stable over time. Thus, periodic validation of the model is necessary for short-term applications. [ABSTRACT FROM AUTHOR]

One of society's most pressing challenges in the twenty-first century is that of climate change. In fact, climate change is seen as the most defining issue of our time as we are witness to an anthropogenic perturbation in geology and earth sciences of global scale. To move forward in this new era, solutions will be sought to both mitigate the effects of climate change (e.g., reduce greenhouse gasses) as well as adapt and build resilience (e.g., improve infrastructure and agriculture to resist damage from extreme weather or floods). The immediacy of the needed solutions dictates that the response must use the full force of society's current knowledge base, science, technology, and innovation. Nanotechnology, an enabling technology that has matured over the past few decades and now considered for general-purpose and mass use, is ideal for addressing climate change and its impacts. To position nanotechnology to address such complex challenges, this Perspective integrates collective insights from a broad range of viewpoints and presents recommendations for how research can be motivated and scoped, organized, and implemented to achieve beneficial outcomes and innovations in the most efficient ways. While this Perspective was created with a focus on the research landscape within the United States, the findings are also relevant in other international contexts. Research that can effectively advance nanotechnology solutions will be use-inspired basic research, incorporate systems-level thinking, apply a convergence research approach, engage stakeholders, and require advanced nanotechnology infrastructure. By illuminating this compelling and complex research topic, this Perspective aims to direct, inform, and accelerate needed actions in the research community to advance nanotechnology solutions for addressing climate change. [ABSTRACT FROM AUTHOR]

The Philadelphia Region is threatened by anthropogenic climate change, with frontline communities facing disproportionate adverse impacts. To be effective, regional plans for climate resilience need to respond to diverse stakeholder needs, with research and information gathering critical to governance and decision‐making. The Climate Resilience Research Agenda (CRRA) is a co‐produced preliminary list of research activities that, if undertaken, could help to make the Philadelphia region more resilient to climate change. The CRRA process engaged over 100 individuals from over 60 organizations and entities across the region, collaboratively producing key research topics across four working groups: 1. Regional Climate Change and Cascading Hazards, 2. Health and Environmental Vulnerability, 3. Built Environment and Infrastructure System Resilience, and Decarbonization/Electrification of the Energy Sector, and 4. Regional Climate Governance and Adaptive Management. Research topics spanned working groups, and connections between topics highlighted the interconnectedness of climate challenges facing the Philadelphia region. In future project phases, the planning team will iterate and improve the recommendations with feedback from the participants from this process to continue important conversations regarding climate resilience within the region. Plain Language Summary: Climate change is a significant issue in the Philadelphia region, with some communities being affected more frequently and intensely than others. To fill gaps in the research that would help the region plan for climate change, the author's team organized a process to establish a Climate Resilience Research Agenda for the region. This process involved over 100 people from different groups who worked together to develop research topics that are summarized in this report. All the topics discussed by the group are interconnected, and the process needs to be continued to make the region more resilient to climate change. Key Points: A Climate Resilience Research Agenda process was undertaken to co‐produce a preliminary list of research activities to make the Philadelphia region more resilient to climate changeOver 100 individuals from 60 different community organizations and entities were engaged and collaboratively produced >200 research topics across four working groupsThe participants reported positive feedback about the engagement, results of this process, interest in further involvement, and the need for increased co‐production in scientific inquiry. Critiques of CRRA hope to be used by the project team or other researchers to iterate on future co‐production processes [ABSTRACT FROM AUTHOR]

This paper assesses how the concepts of social vulnerability and equity are discursively constructed in co‐production research settings with a co‐created decision support tool. The data was collected during workshops (32 communities represented) and interviews (24 communities represented) with stormwater managers and stakeholders (SMS) working in small cities, counties/parishes, and villages and towns within 100 miles of the US Gulf Coast. Findings from participant observation of workshops show that SMS express similar viewpoints regarding indicators of social vulnerability to climate‐related hazards, often defining vulnerable people as less physically mobile or as having few monetary resources. Individuals articulated "community" through a lens of homeownership and often described their relationship to residents as strained. Findings from interviews reveal divergent viewpoints on the imperative and rationale for integrating social vulnerability data into stormwater assessments and planning. Interviews also generated more critical reflection than was present in workshops regarding racial disparities in flood hazard exposure. A key insight from this article is that tools and team composition matter but there is nuance in the degree to which they matter considering a range of contextual factors. The article argues that co‐production teams must plan for alternatives and different scenarios given the messiness of co‐production in practice and that research is needed to reveal the conditions under which specific tools matter for equity in terms of both process and outcomes. Plain Language Summary: This paper assesses how social vulnerability and equity were discussed in workshops and interviews with stormwater managers and stakeholders (SMS) representing small and medium‐sized cities, counties/parishes, and villages and towns within 100 miles of the US Gulf Coast. The workshops (32 communities represented) and interviews (24 communities represented) were part of a project where researchers and practitioners disseminated digital tools to these local stakeholders for conducting stormwater climate vulnerability assessments. In the workshops, SMS expressed similar viewpoints regarding the groups of people who are the most at risk, often defining these groups of people as having mobility impairments or without access to adequate monetary resources. Individuals associated "community" with homeownership and often described their relationships with residents as strained. Findings convey divergent viewpoints on digital tools and social vulnerability data, and the imperative or rationale for integrating social vulnerability data into their work. Interviews generated more critical reflection than was present in workshops regarding racial disparities in exposure to hazards. Authors reflect on the challenges to conducting inclusive facilitation in co‐production settings. A key insight from this article is that tools and team composition matter but there is nuance in the degree to which they matter considering contextual factors. Key Points: Stormwater managers and stakeholders differ in how they discuss social vulnerability and equity in relation to their workLocal government actors often discuss "community" as both an asset and obstacleTools, team composition, and context matter to for co‐production to address racial equity in adaptation [ABSTRACT FROM AUTHOR]

Evaluating the precision and applicability of high-quality precipitation products in the distinctive terrain and intricate climate of the Yunnan-Kweichow Plateau (YKP) is pivotal for climate research. This study comprehensively assesses four gridded precipitation datasets (AERA5-Asia, AIMERG, ERA5-Land, and IMERG-Final) using the China Meteorological Administration's surface precipitation data. It employs eight statistical indicators and error decomposition methods at various spatiotemporal scales. The main findings are as follows: (1) AERA5-Asia, AIMERG, and IMERG-Final show similar precipitation patterns, with ERA5-Land overestimating. While all display minor seasonal variations, AERA5-Asia underestimates summer rain. ERA5-Land tends to overstate, whereas AIMERG and IMERG-Final are generally accurate but slightly undervalued in southern YKP. (2) Hourly analysis reveals AERA5-Asia leads in performance metrics (CC: 0.23, MAE: 0.49 mm/hour, RMSE: 0.18 mm/hour, CSI: 0.27). In contrast, ERA5-Land lags, marked by the lowest BIAS (35.39%), FAR (0.74), and FBI (2.85). AIMERG and IMERG-Final display comparable results but underperform in CC (0.16, 0.13), POD (0.31, 0.30), and CSI (0.19, 0.18). (3) False bias significantly contributes to the total bias of precipitation products. AERA5-Asia and AIMERG mitigate total bias and enhance false precipitation situations through calibration algorithms, albeit introducing missed bias in the central region of YKP. The study findings offer valuable insights into YKP precipitation, informing the development of grid-based fusion algorithms in the region's complex terrain. [ABSTRACT FROM AUTHOR]

Climate instability and devastation pose global threats to human security. Place-based approaches to psychology offer useful orientations towards situating people in time and place, and for advancing research into climate disruptions. Informed by assembling thinking, this article explores the emplaced experiences and emergent agentive responses to climate change among farmers in four sites in the Mekong Delta Region of Vietnam. In August 2023, 20 farmers (5 each from 4 communal sub-regions) took part in interviews where they emplaced their experiences of climate disruptions by drawing their farms, what had changed, and how they were responding. With government and cooperative support, farmers report building water levees, regenerating soil compositions, seeding alternative crops, and changing cultivation practices. These efforts are restrained by the share scale of changes, increased debt burdens, and resource limits. Findings foreground the importance of culture, interconnected identities and emergent agency in the assemblaging of, and changes within local farmscapes and continuing familial legacy place attachments in the region. [ABSTRACT FROM AUTHOR]

Although previous research on climate change in the Arabian Peninsula (AP) has studied changes in individual meteorological variables, an analysis of changes in the overall weather conditions based on multiple meteorological variables is limited. Based on an air mass‐based classification system, this research explored the local climate (1979–2023) of daily surface weather conditions (i.e., air masses) and associated changes over the Peninsula. For this purpose, the gridded weather typing classification (GWTC‐2), an approach to classify multivariate surface weather situations relative to the average local climate, was utilized as it demonstrated outstanding performance in capturing daily weather characteristics in the Peninsula. Cold air mass (C), days with low temperature and near average humidity, was the most common cool weather type, with notable occurrences from Nov to Feb. Warm air mass (W), hot days with near average humidity, was the most common warm weather condition and maintained a marked presence throughout the year, with notable occurrences in summer. Coastal regions showed higher presences of humid warm (HW) and humid (H) days, whereas the central parts demonstrated higher occurrences of air masses of below‐average humidity (dry [D], dry cold [DC], and dry warm [DW]). Much of the Peninsula showed high intra‐annual variability in cool, warm, humid, and dry air masses. Findings from trend analysis reinforce findings from previous studies related to ongoing warming over the Peninsula, as cool weather types (DC, C, and HC) are becoming less frequent, while warm weather types (W, DW, and HW) are becoming more frequent. This analysis further detected decreases in the average weather conditions along with reduced duration of C‐type and increases in the length of warm weather types, further aggravating thermal stress across the AP. [ABSTRACT FROM AUTHOR]

In the context of global warming, the study of climate change and its impacts has garnered significant attention. The Qinghai-Tibet Plateau, with its unique natural environment and socio-cultural significance, plays a crucial role in climate change and the development of the Chinese nation. Investigating the historical interactions, development, and socio-climatic background of the nomadic groups on the Qinghai-Tibet Plateau and agricultural ethnic groups in eastern China contributes to understanding the intrinsic mechanisms of how climate change affects the social interconnectivity of different ethnic groups. This paper is founded on an analysis of 450 conflict events between these groups, as reflected in ancient Chinese war chronicles and ethnic relations annals, creating a decadal conflict sequence from the Eastern Han to the Qing Dynasty (25AD-1910AD). The study conducts a correlation analysis and phase-wise statistical comparison between the climate research findings and the ethnic conflict sequence, specifically examining their relationship with temperature. The main findings are: (1) From the Eastern Han to the Qing Dynasty, a total of 876 conflicts occurred between the ethnic groups in the eastern Qinghai-Tibet Plateau and its vicinity (averaging 4.6 conflicts per decade), with 62.1% initiated by the Ethnic groups on the Qinghai-Tibet Plateau and 66.6% won by the Ethnic groups in eastern China; (2) The frequency of conflicts is primarily correlated with temperatures in the Eastern region, with a less distinct correlation in the Qinghai-Tibet Plateau. During overall warmer periods, conflict incidents were more frequent, with a higher proportion initiated by the Ethnic groups on the Qinghai-Tibet Plateau. Conversely, in colder periods, there were fewer conflicts, with an increased proportion initiated by the Ethnic groups in eastern China; (3) Unlike conflicts between the Ethnic groups in northwestern and eastern China, the Ethnic groups on the Qinghai-Tibet Plateau's external expansion was not a primary objective, indicating that the conflicts were mainly driven by regional economic strength and land productivity. The temperature can reduce land productivity by limiting the growth potential of crops and livestock or by reducing the advance to restrict their production. As a result, water resources, as a condition of land productivity variation, and climate became significant backgrounds for ethnic interactions. [ABSTRACT FROM AUTHOR]

This paper is based on our experience of running a one‐day participatory workshop at the Hull History Centre in the summer of 2023 titled ‘Watery Archives: Exploring Hull's flood histories’. The event was co‐designed by academics and archivists in direct response to feedback from our community participants as part of the Risky Cities project at the University of Hull. During the day, we invited participants to explore original historical material about Hull's flood histories from the seventeenth to the twentieth century, and then to produce creative and sometimes activist outputs that encouraged participants to critically engage with climate change today. The paper reflects on the value of community and participatory based approaches to open up access to archives and to unleash new learnings and discoveries. It considers some of the potential barriers to engagement in archives and shows how we addressed some of those challenges in our workshop design using methods of participatory learning and research. The paper shows how the 'Watery Archives' workshop built on the power of partnership between academic and heritage organisations to drive community‐led, place‐based research. [ABSTRACT FROM AUTHOR]

The evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a "good model". This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models. [ABSTRACT FROM AUTHOR]

Drought is one of the most devastating threats to the livelihoods of the southern African population, who mainly rely on rain‐fed agriculture for income. Previous studies have highlighted that the Botswana High influences drought over the region; however, its influence on the spatial modes of drought remains unknown. This study examines the spatiotemporal structures of drought modes (DMs) over southern Africa and their link with the Botswana High in observation, reanalysis and Model for Prediction Across Scales (MPAS). To characterize droughts, the study uses the 3‐month scale standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). Spatiotemporal characteristics of the DMs are identified using empirical orthogonal function (EOF) analysis on SPI and SPEI. EOF analysis is also used to identify the spatiotemporal characteristics of the Botswana High. The relationship between each DM and the Botswana High is quantified using correlation and R2 analysis. In all the datasets (Climate Research Unit (CRU), European Centre for Medium‐Range Weather Forecasts version 5 (ERA5), 20th Century reanalysis II (20C) and MPAS), the most dominant five DMs (hereafter DM1–DM5) over southern Africa jointly explain more than 60% of the interannual variability in the 3‐month scale summer droughts for SPEI and SPI. CRU, ERA5 and MPAS agree that the Botswana High correlates with the interannual variability of DM1, with a stronger correlation in ERA5 (r = −0.85) compared to MPAS (r = −0.42) and CRU (r = −0.35). Additionally, wet years (+ve SPEI and SPI) are characterized by a weak Botswana High and drought years (−ve SPEI and SPI) by a strong Botswana High. The wet and dry years correspond to the −ve and +ve phases of El Niño–Southern Oscillation (ENSO), respectively. Given this, the results of this study suggest that the Botswana High might be a teleconnection pattern through which ENSO signals influence DM1 over the region. [ABSTRACT FROM AUTHOR]

• A method is devised to enable the quantitative assessment of precipitation precision among GCMs. • GCP6 demonstrates benefits in simulating precipitation probability, correlation, extreme values, and spatial variability. • Significant variations in the precision of precipitation simulations are evident across models, especially the CP6 models. • The top-ten models are selected for providing suitable rainfall simulations in the Hanjiang River Basin. To effectively compare and analyze daily precipitation modeling capabilities across different CMIP6 datasets, our study introduces a novel method to compare daily precipitation models across CMIP6 datasets in the Hanjiang River Basin (HRB). We quantify indicators such as precipitation distribution, temporal correlation, wet-dry detection, extreme value error, and spatio-temporal variability, enabling a comprehensive rating of precipitation accuracy. It has been found that while both CMIP6(CP6) and NEX-GDDP-CMIP6 (GCP6) models show similar simulation accuracy, GCP6 excels in several aspects like distribution, temporal correlation, extreme value simulation, and spatial variability, yet lags in wet-dry detection and temporal change. Notably, using the comprehensive rating score (CRS) analysis, significant differences in precipitation simulation accuracy exist between models, particularly CP6, with variations of up to 0.22 (51.2%) between the highest and lowest scores. Among the top ten models, GCP6 occupies four positions such as MRI-ESM2-0, GFDL-CM4, MPI-ESM1-2-HR, and TaiESM1, while CP6 holds the remaining six like CanESM5, EC-Earth3-Veg, MPI-ESM1-2-HR, GFDL-CM4, MRI-ESM2-0, and IPSL-CM6A-LR. These findings not only offer a clear understanding of the simulation performance of CMIP6 datasets across various precipitation characteristics, but also quantitatively compare the modeling capabilities of different models for watershed precipitation through CRS. This aids climate adaptation research, hydrological forecasting, and flood management in the basin. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness. [ABSTRACT FROM AUTHOR]

Soil moisture is critical in the linkage between the land and atmosphere of energy and water exchange, especially over the Tibetan Plateau (TP). However, due to the lack of in situ plateau soil moisture measurements, the reanalyzed and assimilated data are the major supplements for TP climate research. Based on observations from 1992 to 2013, this study provides a comprehensive evaluation of three sets of assimilation and reanalysis products (GLDAS, ERA5-Land, and MERRA-2) on the climatic mean and variability of soil moisture over the Tibetan Plateau (TPSM). For the climatic mean, GLDAS captures the spatial distribution and annual cycle of TPSM better than other datasets in terms of lower spatial RMSE (0.07 m3×m-3) and bias (0.06 m3×m-3). In terms of the climatic variability of TPSM, the multi-data average (MDA) highlights its advantages in reducing the bias relative to any single data product. MDA describes the TPSM anomalies more stably and accurately in terms of temporal trend and variation (r = 0.94), as well as the dipole spatial pattern in EOF1. When considering both the climatic mean and spatial variability, the performance of MDA is more accurate and balanced than that of a single data product. This study overcomes the deficiency of limited time and space in previous evaluations of TPSM and indicates that multi-data averaging may be a more effective approach in the climate investigation of TPSM. [ABSTRACT FROM AUTHOR]

Our understanding of how rapid Arctic warming and permafrost thaw affect global climate dynamics is restricted by limited spatio-temporal data coverage due to logistical challenges and the complex landscape of Arctic regions. It is therefore crucial to make best use of the available observations, including the integrated data analysis across disciplines and observational platforms. To alleviate the data compilation process for syntheses, cross-scale analyses, earth system models, and remote sensing applications, we introduce ARGO, a new meta-dataset comprised of greenhouse gas observations from various observational platforms across the Arctic and boreal biomes within the polar region of the northern hemisphere. ARGO provides a centralised repository for metadata on carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) measurements linked with an interactive online tool (https://www.bgc-jena.mpg.de/argo/). This tool offers prompt metadata visualisation for the research community. Here, we present the structure and features of ARGO, underscoring its role as a valuable resource for advancing Arctic climate research and guiding synthesis efforts in the face of rapid environmental change in northern regions. The ARGO meta-dataset is openly available for download at Zenodo (https://doi.org/10.5281/zenodo.13870390) (Vogt et al., 2024). [ABSTRACT FROM AUTHOR]

The severe impact of global warming, especially in the arctic region, have a multitude of consequences spanning from sea-level rises and freshening of the ocean, to significant changes to the animal life, biodiversity and species distribution. As the arctic regions are inherently remote and can be both hazardous and difficult to reach, research to improve our understanding of the climate change impact is often limited to short term field-campaigns. Here we present the Danish DISCO-2 student CubeSat mission, designed to meet the growing need for an Earth-observing platform. This mission leverages the rapid advancements in CubeSat technology over the past decades to overcome the limitations of traditional fieldwork campaigns. DISCO-2 will assist on-going arctic climate research with a payload of optical and thermal cameras in combination with novel in-orbit data analysis capabilities. It will further be capable of performing photogrammetric observations to determine ice volumes from deteriorating glaciers and provide surface temperatures, enabling studies of heat transfer between glaciers and arctic fjords. As a student satellite, the payload capabilities will also be offered to novel student research ideas throughout the mission life time. The modularity and wide range of of-the-shelf-components for CubeSats has facilitated an immense opportunity to tailor this earth observing CubeSat to accommodate specific scientific goals and further provided students at the participating universities with an unparalleled possibility to go from an initial research idea to a running CubeSat mission. [ABSTRACT FROM AUTHOR]

The balloon-borne radiosonde observations provide high-resolution profile observations of pressure, temperature, relative humidity, and winds from the surface to the middle stratosphere. These observations help validate space-based data and are used in climate research, weather forecasting. For the large amount of second-level radiosonde data, it is tedious and time-consuming for the manual quality control (QC). Furthermore, varying experiences and different judgment standards may lead to inconsistent judgments for abnormal data. To address these issues, we propose a two-stage QC method for second-level radiosonde data based on the Bezier curve fitting. In the stage QC1, the gross errors are filtered out according to the measurement range of the sensors, the change rates and extreme temperature values based on pressure segmentations. Also, the algorithm of the longest descending sequence(LDS) is used to identify the moment of sounding termination and eliminate items after that moment. In the stage QC2, we score each item with deviations calculated using Bezier curve fitting, and then use a decision tree model ,CART, to identify anomalies in second-level radiosonde data. The experiment results first demonstrate the efficacy of QC at each step, and finally validate the rationality of our method by comparing the statistical characteristics before and after QC. After QC, the error items are greatly reduced, and the percentile profile distribution of temperature, pressure and relative humidity becomes more reasonable. The overlap of items identified by manual QC and automatic QC reaches 86 %, verifying the effectiveness of our method. This research significantly boosts QC efficiency and unifies the QC standards, providing quality assurance for various applications. [ABSTRACT FROM AUTHOR]

This study explored the motivations and challenges vocational teachers (VTs) face in conducting research in underdeveloped regions of Northwest China. We invited 49 vocational teachers from Qinghai province to participate in the questionnaire survey, with their motivation measured using scale items and their challenges measured using open-ended questions. After data collection, latent profile analysis (LPA) was used to explore the participants' motivational profiles, and three types were identified, i.e., high autonomous and controlled motivation, high autonomous and low controlled motivation, and low autonomous motivation. In addition, we conducted a qualitative analysis of the challenges in teacher research. As a result, five categories of challenges that might hinder Chinese vocational teachers in conducting research were observed (i.e., researcher identity, research knowledge and skills, research climate in schools, workload and family care, and resources and financial support). Teachers with the profile of highly autonomous and controlled motivation were more likely to face challenges related to a lack of resources and financial support and the research climate. Workload and family care appeared to be significant challenges for teachers with the profile of highly autonomous but less controlled motivation. In contrast, a lack of research knowledge and skills was a common perceived challenge across all profiles. These results suggest that although vocational teachers express relatively high motivation in conducting research, the significance of institutional development programmes and external research support for research activities remains crucial. [ABSTRACT FROM AUTHOR]

In GNSS (Global Navigation Satellite Systems) meteorology, the accuracy of precipitable water vapor (PWV) retrieved from the tropospheric delay of GNSS signals is affected by the conversion factor. Compact VMF1 product (known as GGOS Atmosphere data) provides high‐accuracy global grid‐wise weighted mean temperature (Tm) values, which can be utilized to calculate the conversion factor. However, the Tm provided in the compact VMF1 data are solely ground surface values. To enhance the performance of compact VMF1 product, a new Tm lapse rate model for each grid point was developed for the purpose of reducing its surface Tm to the elevation of the GNSS site. Then the reduced Tm values over the neighboring grid points together with horizontal interpolation were used to obtain the interpolated Tm for the GNSS station. The sample data for the development of the new model were the Tm profiles obtained from ERA5 monthly averaged data spanning 2009–2018. To assess the model's performance, global radiosonde data at 504 radiosonde stations spanning 2019–2021 were employed. Results demonstrated that implementing the Tm lapse rate model significantly enhanced the accuracy of interpolating Tm values for GNSS stations with substantial height disparities from adjacent grid points, thereby improving PWV conversion accuracy. This indicates that employing the new Tm lapse rate model to adjust surface Tm data in the compact VMF1 product holds promise for enhancing its utility in GNSS meteorology. Plain Language Summary: Global Navigation Satellite Systems (GNSS), including GPS, GLONASS, GALILEO and BDS, are powerful tools for retrieving precipitable water vapor (PWV), a crucial parameter in weather and climate research. The process involves estimating the wet delay embedded in GNSS signals caused by water vapor in the atmosphere and then converting the delay into PWV using a conversion factor based on the weighted mean temperature (Tm). Compact VMF1 product provides high‐accuracy ground surface grid‐wise Tm values, however, when the altitude of the GNSS site largely differs from that of the nearby grid points, the accuracy of the Tm interpolated from the surface Tm may be poor. To improve the accuracy of Tm interpolation and better PWV inversion, we developed a grid‐wise Tm lapse rate model that adjusts for altitude differences. Key Points: A Tm ${T}_{m}$ lapse rate model was developed to enhance the performance of the grid‐wise compact VMF1 Tm ${T}_{m}$ dataThe accuracy of the predicted Tm ${T}_{m}$ and PWV was significantly improvedThe new model adds a good value to GNSS meteorology for better performance [ABSTRACT FROM AUTHOR]

Reanalysis datasets, such as ERA5, are essential for climate research, offering comprehensive spatiotemporal coverage. However, their accuracy needs thorough evaluation for effective regional application, particularly in areas with complex topography like the Poyang Lake Basin (PLB), China's largest freshwater lake. This study evaluated ERA5's accuracy in simulating near-surface air temperature and precipitation in the PLB, using data from 24 meteorological stations. Key metrics, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) were applied across daily, monthly, seasonal, and annual scales. The results show that ERA5 performs well for daily mean temperature, with daily R values above 0.98 and RMSEs ranging from 0.95 °C to 3.11 °C. Its highest accuracy was in February and March, with R values exceeding 0.95, and seasonal trends were best captured in spring and autumn (R > 0.99). However, ERA5's performance for precipitation was less accurate, with daily R values between 0.578 and 0.687 and RMSEs between 8.58 mm and 11.10 mm. ERA5 consistently overestimated precipitation, particularly during 1980–2003. These findings highlight ERA5's strengths in temperature modeling and its limitations in precipitation, providing insights for identifying climate events and improving climate simulation in the PLB. [ABSTRACT FROM AUTHOR]

In this study, we visualize and analyze the literature on the Brahmaputra river using a spectral clustering algorithm, tracking research trends over time. We found that the focus of research on the Brahmaputra has changed over time in the last decade, with a shift from geology to hydrology and geochemistry and a rapid growth in climate change research in recent years. In the future, potential hot topics may be "water resource management" and other topics related to transboundary water resource management and cooperation. At the same time, this study also analyzes in detail the keywords and clusters "geohydrology" and "ecological risk and sustainable development", among other topics. We believe that future research should carefully consider the potential effects of transdisciplinary research trends. For instance, it is urgent that transborder governance and management regimes be renovated through joint efforts and cross-border effective actions carried out by multifaceted and multi-scalar agencies along this river. [ABSTRACT FROM AUTHOR]