Climate

33 pages, 2502 KB

Open AccessReview

A Review of Heat Wave Impacts on the Food–Energy–Water Nexus and Policy Response

by Manman Wang, Sze Yui Lu, Hairong Xin, Yuxuan Fan, Hao Zhang, Sujata Saunik and Rajib Shaw

Climate 2026, 14(1), 27; https://doi.org/10.3390/cli14010027 - 21 Jan 2026

Viewed by 393

Abstract

Heat waves have emerged as an escalating climate threat, triggering cascading disruptions across food, energy, and water systems, thereby undermining resilience and sustainability. However, reviews addressing heat wave impacts on the food–energy–water (FEW) nexus remain scarce, resulting in a fragmented understanding of cross-system [...] Read more.

Heat waves have emerged as an escalating climate threat, triggering cascading disruptions across food, energy, and water systems, thereby undermining resilience and sustainability. However, reviews addressing heat wave impacts on the food–energy–water (FEW) nexus remain scarce, resulting in a fragmented understanding of cross-system interactions and limiting the ability to assess cascading risks under extreme heat. This critical issue is examined through bibliometric analysis, scoping review, and policy analysis. A total of 103 publications from 2015 to 2024 were retrieved from Web of Science and Scopus, and 63 policy documents from the United States, the European Union, Japan, China, and India were collected for policy analysis. Bibliometric analysis was conducted to identify the most influential articles, journals, countries, and research themes in this field. The scoping review indicates that agricultural losses are most frequently reported (32), followed by multiple impacts (19) and cross-sectoral disruptions (18). The use of spatial datasets and high-frequency temporal data remains limited, and community-scale studies and cross-regional comparisons are uncommon. Mechanism synthesis reveals key pathways, including direct system-specific stress on food production, water availability, and energy supply; indirect pressures arising from rising demand and constrained supply across interconnected systems; cascading disruptions mediated by infrastructure and system dependencies; and maladaptation risks associated with uncoordinated sectoral responses. Policy analysis reveals that most countries adopt sector-based adaptation approaches with limited across-system integration, and insufficient data and monitoring infrastructures. Overall, this study proposes an integrated analytical framework for understanding heat wave impacts on the FEW nexus, identifies critical research and governance gaps, and provides conceptual and practical guidance for advancing future research and strengthening coordinated adaptation across food, energy, and water sectors. Full article

(This article belongs to the Special Issue Climate Change and Food Sustainability: A Critical Nexus)

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10 pages, 322 KB

Open AccessTechnical Note

Small and Medium-Sized Enterprises Climate Accounting Made Easy

by Hans Sanderson, Mariana Costa Moreira Maia, Frank Akowuge Dugasseh, Delove Abraham Asiedu and Annabeth Aagaard

Climate 2026, 14(1), 26; https://doi.org/10.3390/cli14010026 - 21 Jan 2026

Viewed by 106

Abstract

The European Union’s decarbonization strategy relies on transparent and accurate climate data across value chains. Yet, existing sustainability reporting frameworks mainly target large companies, often neglecting small and medium-sized enterprises (SMEs). Although SMEs are largely exempt from mandatory reporting under recent regulatory simplifications, [...] Read more.

The European Union’s decarbonization strategy relies on transparent and accurate climate data across value chains. Yet, existing sustainability reporting frameworks mainly target large companies, often neglecting small and medium-sized enterprises (SMEs). Although SMEs are largely exempt from mandatory reporting under recent regulatory simplifications, they play a critical role in Scope 3 emissions, which dominate the carbon footprints of larger firms. This paper presents two complementary, freely accessible digital tools designed to support credible carbon accounting. The first tool, Climate Compass, is a government-sanctioned tool that aligns with the GHG Protocol and has been used by >10,000 SMEs in Denmark to calculate Scopes 1, 2, and 3 emissions through a user-friendly interface. The second, a newly developed online cradle-to-gate life cycle assessment (LCA) tool, supports product-level carbon footprinting using open-source emission factor databases. The cradle-to-gate approach reflects typical SME production profiles and emphasizes embodied CO₂e from raw materials, transport, and energy consumption. Together, these tools enable researchers to effectively assess SMEs emissions in the value chain and thus support decarbonization while supplying reliable data to larger companies. The tool democratizes emissions analysis and supports regulatory and market demands and strengthens SMEs contribution to Europe’s low-carbon transition. Full article

(This article belongs to the Topic Climate Change and Aquatic Ecosystems: Impacts, Mitigation and Adaptation)

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23 pages, 661 KB

Open AccessArticle

Farmers’ Perception of Improved Rice Varieties for Climate Change Adaptation in Batang Regency, Indonesia

by Anggi Sahru Romdon, Ratih Kurnia Jatuningtyas, Yayat Hidayat, Munir Eti Wulanjari, Cahyati Setiani, Afrizal Malik, Joko Triastono, Resmayeti Purba, Bahtiar Bahtiar, Dewa Ketut Sadra Swastika, Dedi Sugandi, Raden Heru Praptana, Bambang Nuryanto, Hermawati Cahyaningrum, Muji Rahayu, Joko Pramono, Wahyu Wibawa, Miranti Dian Pertiwi, Forita Dyah Arianti and Komalawati Komalawati

Climate 2026, 14(1), 25; https://doi.org/10.3390/cli14010025 - 20 Jan 2026

Viewed by 290

Abstract

Farmers’ perceptions of improved rice varieties represent a critical initial step in their adoption as climate change adaptation strategies. This study examined farmers’ perceptions by integrating on-farm adaptive research, which compared the agronomic performance of rice varieties, with participatory approaches to capture farmers’ [...] Read more.

Farmers’ perceptions of improved rice varieties represent a critical initial step in their adoption as climate change adaptation strategies. This study examined farmers’ perceptions by integrating on-farm adaptive research, which compared the agronomic performance of rice varieties, with participatory approaches to capture farmers’ evaluation of improved varieties. A total of 81 farmers from climate-affected areas of Batang Regency were purposively selected as respondents. Data was collected through structured interviews and questionnaires administered during the evaluation of field demonstrations. Farmers’ perception levels were assessed using a Guttman scale and classified into three categories: high, medium, and low. Logistic regression analysis was subsequently employed to examine the relationship between farmers’ socio-demographic characteristics and their acceptance of improved rice varieties. The results indicate that, overall, farmers exhibited a low perception of improved rice varieties. Among the evaluated opinions, Inpari 32 HDB received the highest perception scores across all agronomic attributes. The regression results show that farm size and age significantly influence variety acceptance. The odds ratio for farm size (0.117) suggests that each additional hectare of cultivated land area reduces the likelihood of adopting improved rice varieties by approximately 88.3%, holding other factors constant. In contrast, the odds ratio for age (1.080) indicates that each additional year of age increases the probability of adoption by about 8%. Full article

(This article belongs to the Special Issue Climate Change Impacts at Various Geographical Scales (2nd Edition))

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22 pages, 1523 KB

Open AccessArticle

Short-Term Heavy Rainfall Potential Identification Driven by Physical Features: Model Development and SHAP-Based Mechanism Interpretation

by Jingjing An, Jie Liu, Dongyong Wang, Huimin Li, Chen Yao, Ruijiao Wu and Zhaoye Wu

Climate 2026, 14(1), 24; https://doi.org/10.3390/cli14010024 - 20 Jan 2026

Viewed by 130

Abstract

Accurate analysis and forecasting of short-term heavy rainfall (hourly rainfall ≥ 20 mm) are crucial for extending warning, enabling targeted preventive measures, and supporting efficient resource allocation. In recent years, machine learning techniques combined with atmospheric physical variables have offered promising new approaches [...] Read more.

Accurate analysis and forecasting of short-term heavy rainfall (hourly rainfall ≥ 20 mm) are crucial for extending warning, enabling targeted preventive measures, and supporting efficient resource allocation. In recent years, machine learning techniques combined with atmospheric physical variables have offered promising new approaches for analyzing and predicting and forecasting short-term heavy rainfall. However, these methods often lack transparency, which hinders the interpretation of key atmospheric physical variables that drive short-term heavy rainfall and their coupling mechanisms. To address this challenge, the present study integrates the interpretable SHAP (SHapley Additive exPlanations) framework with machine learning to examine potential relationships between widely used atmospheric physical variables and short-term heavy rainfall, thereby improving model interpretability. CatBoost models were constructed based on multiple feature-input strategies using 71 physical variables across five categories derived from ERA5 reanalysis data, and their performance was compared with two benchmark algorithms, XGBoost and LightGBM. The SHAP method was subsequently applied to quantify the contributions of individual features and their interaction effects on model predictions. The results indicate that (1) the CatBoost model, utilizing all 71 physical variables, outperforms other feature combinations, with an AUC of 0.933, and F1 score of 0.930, and a Recall of 0.954, significantly higher than the XGBoost and LightGBM models; (2) Shapley value analysis identified 500 hPa vertical velocity, the A-index, and precipitable water as the most influential features on model performance; (3) The predictive mechanism for short-term heavy rainfall is fundamentally bifurcated: negative instances are classified through the discrete main effects of individual features, whereas positive event detection necessitates a sophisticated coordination of intrinsic main effects and synergistic interactions. Among the feature categories, the horizontal and vertical wind fields, stability and energy indices, and humidity-related variables exhibited the highest contribution ratios, with wind field features demonstrating the strongest interaction effects. The results confirm that integrating atmospheric physical variables with the CatBoost ensemble learning approach significantly improves short-term heavy rainfall identification. Furthermore, incorporating the SHAP interpretability framework provides a theoretical foundation for elucidating the mechanisms of feature influence and optimizing model performance. Full article

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40 pages, 2191 KB

Open AccessArticle

A Climate–Geomechanics Interface for Adaptive and Resilient Geostructures

by Tamara Bračko and Bojan Žlender

Climate 2026, 14(1), 23; https://doi.org/10.3390/cli14010023 - 19 Jan 2026

Viewed by 207

Abstract

Geostructures, such as foundations, embankments, retaining structures, bridge abutments, and both natural and engineered slopes, interact with the ground to ensure structural safety and functionality. One significant factor influencing these systems is climate, which continuously affects soil conditions through dynamic processes. Over the [...] Read more.

Geostructures, such as foundations, embankments, retaining structures, bridge abutments, and both natural and engineered slopes, interact with the ground to ensure structural safety and functionality. One significant factor influencing these systems is climate, which continuously affects soil conditions through dynamic processes. Over the past century, climate change has intensified, increasing uncertainties regarding the safety of both existing and planned geostructures. While the impacts of climate change on geostructures are evident, effective methods to address them remain uncertain. This paper presents an approach for mitigating and adapting to climate change impacts through a stepwise geomechanical analysis and geotechnical design framework that incorporates expected climatic conditions. A novel framework is introduced that systematically integrates projected climate scenarios into geomechanical modeling, enabling climate-resilient design of geostructures. The concept establishes an interface between climate effects and geomechanical data, capturing the causal chain of climate hazards, their effects, and potential consequences. The proposed interface provides a practical tool for integrating climate considerations into geotechnical design, supporting adaptive and resilient infrastructure planning. The approach is demonstrated across different geostructure types, with a detailed slope stability analysis illustrating its implementation. Results show that the interface, reflecting processes such as water infiltration, soil hydraulic conductivity, and groundwater flow, is often critical to slope stability outcomes. Furthermore, slope stability can often be maintained through simple, timely implemented nature-based solutions (NbS), whereas delayed actions typically require more complex and costly interventions. Full article

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14 pages, 4046 KB

Open AccessArticle

Fragmentary Weather Records from Cádiz (Spain) in the 18th Century: Insights from Archival and Library Sources

by José Manuel Vaquero and María Cruz Gallego

Climate 2026, 14(1), 22; https://doi.org/10.3390/cli14010022 - 17 Jan 2026

Viewed by 325

Abstract

This study focuses on the recovery and digitization of three fragmentary meteorological datasets from the archives of the Royal Observatory of the Spanish Navy in Cádiz, covering selected days in 1776, 1788, and 1793. These records include temperature, pressure, and occasional wind observations [...] Read more.

This study focuses on the recovery and digitization of three fragmentary meteorological datasets from the archives of the Royal Observatory of the Spanish Navy in Cádiz, covering selected days in 1776, 1788, and 1793. These records include temperature, pressure, and occasional wind observations originally linked to astronomical measurements. After manual transcription and quality control, the historical data were compared with long-term climate statistics from the period 1955–2021 for Cádiz. Despite the absence of metadata on instruments and installation, the 18th-century observations show reasonable agreement with present-day seasonal patterns, indicating their reliability. Wind data, although limited, were documented using an eight-point wind rose and terminology consistent with historical standards. These findings highlight the scientific and historical value of scattered early observations. They provide reference points for validating historical reanalysis and suggest that additional records may exist in naval archives. Continued efforts to recover such data will improve long-term climate reconstructions for southern Spain and beyond. Full article

(This article belongs to the Special Issue The Importance of Long Climate Records (Second Edition))

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32 pages, 4385 KB

Open AccessArticle

Probabilistic Wind Speed Forecasting Under at Site and Regional Frameworks: A Comparative Evaluation of BART, GPR, and QRF

by Khaled Haddad and Ataur Rahman

Climate 2026, 14(1), 21; https://doi.org/10.3390/cli14010021 - 15 Jan 2026

Viewed by 158

Abstract

Reliable probabilistic wind speed forecasts are essential for integrating renewable energy into power grids and managing operational uncertainty. This study compares Quantile Regression Forests (QRF), Bayesian Additive Regression Trees (BART), and Gaussian Process Regression (GPR) under at-site and regional pooled frameworks using 21 [...] Read more.

Reliable probabilistic wind speed forecasts are essential for integrating renewable energy into power grids and managing operational uncertainty. This study compares Quantile Regression Forests (QRF), Bayesian Additive Regression Trees (BART), and Gaussian Process Regression (GPR) under at-site and regional pooled frameworks using 21 years (2000–2020) of daily wind data from eleven stations in New South Wales and Queensland, Australia. Models are evaluated via strict year-based holdout validation across seven metrics: RMSE, MAE, R², bias, correlation, coverage, and Continuous Ranked Probability Score (CRPS). Regional QRF achieves exceptional point forecast stability with minimal RMSE increase but suffers persistent under-coverage, rendering probabilistic bounds unreliable. BART attains near-nominal coverage at individual sites but experiences catastrophic calibration collapse under regional pooling, driven by fixed noise priors inadequate for spatially heterogeneous data. In contrast, GPR maintains robust probabilistic skill regionally despite larger point forecast RMSE penalties, achieving the lowest overall CRPS and near-nominal coverage through kernel-based variance inflation. Variable importance analysis identifies surface pressure and minimum temperature as dominant predictors (60–80%), with spatial covariates critical for regional differentiation. Operationally, regional QRF is prioritised for point accuracy, regional GPR for calibrated probabilistic forecasts in risk-sensitive applications, and at-site BART when local data suffice. These findings show that Bayesian machine learning methods can effectively navigate the trade-off between local specificity and regional pooling, a challenge common to wind forecasting in diverse terrain globally. The methodology and insights are transferable to other heterogeneous regions, providing guidance for probabilistic wind forecasting and renewable energy grid integration. Full article

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22 pages, 2529 KB

Open AccessArticle

Comprehensive Tool for Assessing Farmers’ Knowledge and Perception of Climate Change and Sustainable Adaptation: Evidence from Himalayan Mountain Region

by Nirmal Kumar Patra, Limasangla A. Jamir and Tapan B. Pathak

Climate 2026, 14(1), 20; https://doi.org/10.3390/cli14010020 - 15 Jan 2026

Viewed by 338

Abstract

Knowledge and perceptions are prerequisites for contributing to CC mitigation and adaptation. This paper developed a framework and a tool (scale) to capture farmers’ knowledge and perceptions of all aspects of CC. We involved 15 extremely qualified (those with PhD degrees in agriculture [...] Read more.

Knowledge and perceptions are prerequisites for contributing to CC mitigation and adaptation. This paper developed a framework and a tool (scale) to capture farmers’ knowledge and perceptions of all aspects of CC. We involved 15 extremely qualified (those with PhD degrees in agriculture and allied disciplines and experience in scale construction and CC research) experts and 83 highly qualified (a minimum of a PhD degree in agriculture and allied fields was the prerequisite criterion for acting as a judge) judges in the construction of this scale. Further, we adopted factor analysis to draw valid conclusions. We proposed 138 items/statements related to 14 dimensions/issues (General, GHGs, Temperature, Rainfall, Agricultural emissions, shifting cultivation, rice cultivation, Mitigation, C-sequestration, Impact on Agriculture, Livestock, Wind, Natural disaster, Impact, and Adaptation) associated with agriculture and CC scenarios. Finally, 102 items/statements were retained with six indicators/dimensions. The results indicate that the scale explains 83% of variance. The scale is highly consistent (Cronbach alpha = 0.985) and widely applicable to future research and policy decisions. Further, the scale was adopted (with 100 respondents) to assess consistency and validity. Finally, the tool (scale) for assessing farmers’ knowledge and perceptions of CC was prepared for further use and replication. The policy and research system may adopt the framework and scale to assess stakeholders’ inclusive knowledge and perceptions of CC. The findings of this study may be helpful for policymakers, researchers, development workers, and extension functionaries. Full article

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32 pages, 2775 KB

Open AccessReview

AIoT at the Frontline of Climate Change Management: Enabling Resilient, Adaptive, and Sustainable Smart Cities

by Claudia Banciu and Adrian Florea

Climate 2026, 14(1), 19; https://doi.org/10.3390/cli14010019 - 15 Jan 2026

Viewed by 223

Abstract

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and [...] Read more.

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

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21 pages, 378 KB

Open AccessArticle

Can Climate Transition Risks Enhance Enterprise Green Innovation? An Analysis Employing a Dual Regulatory Mechanism

by Liping Cao and Fengqi Zhou

Climate 2026, 14(1), 18; https://doi.org/10.3390/cli14010018 - 15 Jan 2026

Viewed by 173

Abstract

In the context of the global pursuit of the ‘carbon neutrality’ objective, Chinese enterprises are proactively advancing green development and low-carbon transformation. Among these efforts, climate transition risks have emerged as a crucial factor affecting strategic enterprise decisions and long-term competitiveness. This study [...] Read more.

In the context of the global pursuit of the ‘carbon neutrality’ objective, Chinese enterprises are proactively advancing green development and low-carbon transformation. Among these efforts, climate transition risks have emerged as a crucial factor affecting strategic enterprise decisions and long-term competitiveness. This study utilizes a sample comprising Chinese A-share listed enterprises over the period from 2012 to 2024 to construct an enterprise climate transition risk index using text analysis methods. It empirically investigates this index’s impact on enterprise green innovation by adopting panel data analysis method to construct a fixed effects model and further examines the moderating roles of institutional investors’ shareholding and enterprise environmental uncertainties in response to climate transition risks. The research findings indicate the following: First, climate transition risks significantly enhance enterprise green innovation. The validity of this conclusion persists following a series of robustness and endogeneity tests, including replacing the explained variable, lagging the explanatory variable, controlling for city-level fixed effects, and applying instrumental variable methods. Second, both institutional investors’ shareholding and enterprise environmental uncertainties exert a significant positive regulatory effect on the relationship between climate transition risk and green innovation, indicating that external monitoring and heightened risk perception jointly enhance enterprises’ responsiveness in driving green innovation. Thirdly, heterogeneity analysis indicates that the positive impact of climate transition risks on green innovation is notably amplified within non-state-owned enterprises and manufacturing enterprises. By examining the dual regulatory mechanisms of ‘external monitoring’ and ‘risk perception’, this study broadens the study framework on the relationship between climate risks and enterprise green innovation, offering new empirical evidence supporting the applicability of the ‘Porter Hypothesis’ within the context of climate-related challenges. Furthermore, it provides valuable implications for policymakers in refining climate information disclosure policies and assists enterprises in developing forward-looking green innovation strategies. Full article

(This article belongs to the Special Issue Climate Change Adaptation Costs and Finance)

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16 pages, 220 KB

Open AccessArticle

Climate-Conscious Medication Therapy Management: Perspectives of Canadian Primary Care Pharmacists

by Zubin Austin and Paul Gregory

Climate 2026, 14(1), 17; https://doi.org/10.3390/cli14010017 - 15 Jan 2026

Viewed by 192

Abstract

(1) Background: The climate impact of health care work has raised interest in climate-conscious health care practice. Medications contribute significantly to the carbon footprint; there has been insufficient work describing climate-conscious medication therapy management practices that could be useful to address climate change [...] Read more.

(1) Background: The climate impact of health care work has raised interest in climate-conscious health care practice. Medications contribute significantly to the carbon footprint; there has been insufficient work describing climate-conscious medication therapy management practices that could be useful to address climate change caused by health care work. (2) Methods: This exploratory qualitative research study focused on climate-conscious medication therapy management practices. A semi-structured interview protocol was used. A total of 17 primary care pharmacists were interviewed (following informed consent) to the point of thematic saturation. A constant-comparative analysis was undertaken to identify and categorize themes. The research was undertaken based on a protocol approved by the University of Toronto Research Ethics Board. (3) Result: Three main themes emerged: (a) There is insufficient evidence currently available to guide climate conscious medication therapy management; (b) seven specific climate-conscious medication therapy management strategies were identified as being most likely to be acceptable by primary care pharmacists; (c) medication therapy management services focused on climate adaptation strategies for patients should be expanded; (4) Conclusions: As medications become the primary intervention used in health care, climate-conscious medication therapy management becomes more essential than ever. Further work in providing evidence to guide climate-conscious prescribing decisions is needed. Full article

(This article belongs to the Section Climate Adaptation and Mitigation)

19 pages, 1587 KB

Open AccessArticle

Determinants of Spatial Variation in Vulnerability to Extreme Temperatures in Austria from 1970 to 2020

by Hanns Moshammer, Martin Jury, Hans-Peter Hutter and Peter Wallner

Climate 2026, 14(1), 16; https://doi.org/10.3390/cli14010016 - 13 Jan 2026

Viewed by 162

Abstract

Vulnerability to heat and cold is influenced by many characteristics. This study analyzed determinants of vulnerability at the district level in the whole of Austria. Daily deaths (1970–2020) and daily temperatures per district were entered into time series models using negative binomial General [...] Read more.

Vulnerability to heat and cold is influenced by many characteristics. This study analyzed determinants of vulnerability at the district level in the whole of Austria. Daily deaths (1970–2020) and daily temperatures per district were entered into time series models using negative binomial General Additive Models controlling for long-term and seasonal trends and for the day of the week. District-wise effect estimates of 111 districts in total were entered into linear meta-regression models seeking determinants of inter-district variation in heat and cold vulnerability. Generally, temperature effects on the daily number of deaths were highly significant in all districts, with higher death rates occurring when the same-day temperature exceeded a clear threshold and higher death rates with declining temperature averaged over the previous 14 days, in that case not showing any clear threshold effect. A higher heat vulnerability was observed for more densely populated areas, especially for the city of Vienna, for districts with a higher percentage of singles, of homeless people, of unemployed, and of migrants. Surprisingly, a higher percentage of outdoor workers seemed to be protective. Higher cold vulnerability was found for an increasingly autochthonous population, for districts with a higher employment rate, with more commuters, more agricultural workers, and more green spaces. Full article

(This article belongs to the Section Weather, Events and Impacts)

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14 pages, 4201 KB

Open AccessArticle

Under the Heat of Tradition: Thermal Comfort During Summer Correfocs in Catalonia (1950–2023)

by Jon Xavier Olano Pozo, Anna Boqué-Ciurana and Òscar Saladié

Climate 2026, 14(1), 15; https://doi.org/10.3390/cli14010015 - 8 Jan 2026

Viewed by 684

Abstract

Cultural practices such as Catalonia’s correfocs (fire parades) represent a vibrant expression of intangible heritage. Outdoor activities are conditioned by weather and threatened by climate change. This study analyses the long-term evolution of night-time thermal conditions during correfoc festivals performed in six Catalan [...] Read more.

Cultural practices such as Catalonia’s correfocs (fire parades) represent a vibrant expression of intangible heritage. Outdoor activities are conditioned by weather and threatened by climate change. This study analyses the long-term evolution of night-time thermal conditions during correfoc festivals performed in six Catalan towns located on the coast and in the pre-coastal region from 1950 to 2023, using reanalysis-based indicators of air temperature, humidity, and perceived heat as a first exploratory step prior to incorporating in situ meteorological records. Specifically, the Heat Index (HI) and the Universal Thermal Climate Index (UTCI) were computed for the typical event window (21:00–23:00 local time) to assess changes in human thermal comfort. Results reveal a clear and statistically significant warming trend in most pre-coastal locations—particularly Reus, El Vendrell, and Vilafranca—while coastal cities such as Barcelona exhibit weaker or non-significant changes, likely due to maritime moderation. The frequency and intensity of positive temperature anomalies have increased since the 1990s, with a growing proportion of events falling into “caution” or “moderate heat stress” categories under HI and UTCI classifications. These findings demonstrate that correfocs are now celebrated under markedly warmer night-time conditions than in the mid-twentieth century, implying a tangible rise in thermal discomfort and potential safety risks for participants. By integrating climatic and cultural perspectives, this research shows that rising night-time heat can constrain attendance, participation conditions, and event scheduling for correfocs, thereby directly exposing weather-sensitive form of intangible cultural heritage to climate risks. It therefore underscores the need for climate adaptation frameworks and to promote context-specific strategies to sustain these community-based traditions under ongoing Mediterranean warming. Full article

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23 pages, 8400 KB

Open AccessArticle

Seasonal Drought Dynamics in Kenya: Remote Sensing and Combined Indices for Climate Risk Planning

by Vincent Ogembo, Samuel Olala, Ernest Kiplangat Ronoh, Erasto Benedict Mukama and Gavin Akinyi

Climate 2026, 14(1), 14; https://doi.org/10.3390/cli14010014 - 7 Jan 2026

Viewed by 455

Abstract

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical [...] Read more.

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions. Full article

(This article belongs to the Section Climate and Environment)

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21 pages, 4528 KB

Open AccessArticle

Developing an Adaptive Capacity Framework for Women Market Vendors in Vanuatu

by Jessica Kilroy, Karen E. McNamara and Bradd Witt

Climate 2026, 14(1), 13; https://doi.org/10.3390/cli14010013 - 5 Jan 2026

Viewed by 350

Abstract

Pacific Island communities have long navigated the challenges of climate change. Supporting adaptation options is critical for protecting livelihoods, especially given that these countries will continue to unfairly bear the brunt of global climate change impacts. Understanding and strengthening the capacity of individuals [...] Read more.

Pacific Island communities have long navigated the challenges of climate change. Supporting adaptation options is critical for protecting livelihoods, especially given that these countries will continue to unfairly bear the brunt of global climate change impacts. Understanding and strengthening the capacity of individuals and communities to adapt is vital to ensure effective options are available. However, adaptive capacity is highly context-specific and explicit examples, particularly from the Pacific, remain limited. This study focuses on the experiences of women market vendors, for whom marketplaces are integral to food security, income generation, and cultural and social life. Building on existing global and regional frameworks, we assess the adaptive capacity of market vendors across Vanuatu through interviews with women market vendors (n = 69) and key informants (n = 18). The findings informed the development of a new, tailored adaptive capacity framework that identifies six key drivers: access to tangible resources, human assets, social assets, livelihood diversity and flexibility, systems of influence and mindsets, and decision-making capacity. This study presents a context-specific framework grounded in empirical evidence, offering insights for development and adaptation initiatives that aim to strengthen adaptive capacity. We encourage further research to apply and refine this framework across diverse Pacific contexts and sectors to deepen understanding of adaptive capacity and inform effective adaptation strategies. Full article

(This article belongs to the Section Climate Adaptation and Mitigation)

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37 pages, 1416 KB

Open AccessReview

Climate Change and Health Systems: A Scoping Review of Health Professionals’ Perceptions and Readiness for Action

by Vasileios Gkouliaveras, Stavros Kalogiannidis, Dimitrios Kalfas, Apostolia Papaklonari and Stamatis Kontsas

Climate 2026, 14(1), 12; https://doi.org/10.3390/cli14010012 - 4 Jan 2026

Viewed by 977

Abstract

Climate change is one of the greatest challenges of our time, with direct implications for sustainable development, the physical and mental health of populations, and the functioning of health systems. Strengthening the resilience and sustainability of health systems through mitigation and adaptation strategies [...] Read more.

Climate change is one of the greatest challenges of our time, with direct implications for sustainable development, the physical and mental health of populations, and the functioning of health systems. Strengthening the resilience and sustainability of health systems through mitigation and adaptation strategies requires the active involvement of health professionals. This scoping review explores health professionals’ perceptions of climate change and its impacts on public health and health systems, as well as their operational preparedness and the barriers to adaptation. The literature review was conducted in three phases (20 December 2024, 20 January 2025, and 20 March 2025) using the Web of Science, Scopus, and PubMed databases, covering the period 2016–2025 and following PRISMA guidelines. Of the 1888 studies initially identified, 36 met the predefined inclusion and exclusion criteria. The findings showed that while health professionals recognize climate change as a current threat to public health and health systems, they are not adequately prepared to address its impacts. The main barriers to addressing climate change are related to a lack of information and awareness, inadequate training, limited time, lack of supportive leadership, failure to integrate sustainable practices into daily clinical practice and, above all, inadequate funding. Based on these findings, there is an urgent need to develop policies that promote the active participation of health professionals in the design and implementation of climate change mitigation and adaptation strategies. At the same time, there is a need to strengthen research activity through both synchronous and diachronic studies in order to gather information on the sustainability and resilience of health systems. Full article

(This article belongs to the Special Issue Climate Change, Health and Multidisciplinary Approaches)

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15 pages, 2261 KB

Open AccessArticle

Exploring the Potential of Buried Pipe Systems to Reduce Cooling Energy Consumption of Agro-Industrial Buildings Under Climate Change Scenarios: A Study in a Tropical Climate

by Luciane Cleonice Durante, Ivan Julio Apolonio Callejas, Alberto Hernandez Neto and Emeli Lalesca Aparecida da Guarda

Climate 2026, 14(1), 11; https://doi.org/10.3390/cli14010011 - 31 Dec 2025

Viewed by 359

Abstract

Agro-industrial facilities host processes and products that are highly sensitive to thermal fluctuations. Given the projected increase in air temperatures in tropical regions due to climate change, improving indoor thermal conditions in these facilities has become critically important. Conventional cooling systems are widely [...] Read more.

Agro-industrial facilities host processes and products that are highly sensitive to thermal fluctuations. Given the projected increase in air temperatures in tropical regions due to climate change, improving indoor thermal conditions in these facilities has become critically important. Conventional cooling systems are widely used to maintain adequate indoor temperatures; however, they are associated with high energy consumption. In this context, Ground Source Heat Pump (GSHP) technology emerges as a promising alternative to reduce cooling loads by exchanging heat with the ground. This study evaluates the reductions in cooling energy consumption and the return on investment of a GSHP system integrated with conventional cooling system, considering a prototype agro-industrial room located in two ecotones of the Brazilian Midwest: the Amazon Forest (AF) and Brazilian Savanna (BS). Building energy simulations were performed using EnergyPlus software v. 9 under current climate conditions and climate change scenarios for 2050 and 2080. Initially, the prototype room was conditioned using a conventional HVAC system; subsequently, a GSHP system was integrated to enhance energy efficiency and reduce energy demand. Under current conditions, cooling energy demand in the BS and AF ecotones is projected to increase by 16.5% and 18.3% by 2050, and by 24.5% and 23.5% by 2080, respectively. The payback analysis indicates that the average return on investment improves under future climate scenarios, decreasing from 14.5 years under current conditions to 10.13 years in 2050 and 9.86 years in 2080. The findings contribute to understanding the thermal resilience and economic feasibility of ground-coupled heat exchangers as a sustainable strategy for mitigating climate change impacts in the agro-industrial sector. Full article

(This article belongs to the Section Climate and Environment)

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25 pages, 8481 KB

Open AccessArticle

Long-Term Hourly Temperature Dynamics on Tropical Hainan Island (1940–2022)

by Yihang Xing, Chenxiao Shi, Yue Jiao, Ming Shang, Jianhua Du and Lei Bai

Climate 2026, 14(1), 9; https://doi.org/10.3390/cli14010009 - 30 Dec 2025

Viewed by 837

Abstract

With global warming, tropical islands, as sensitive areas to climate change, exhibit new and significant temperature variation characteristics. Using the high-resolution Hainan Island Regional Reanalysis (HNR) dataset and multi-source data, this study analyzes temperature changes on Hainan Island from 1900 to 2022, focusing [...] Read more.

With global warming, tropical islands, as sensitive areas to climate change, exhibit new and significant temperature variation characteristics. Using the high-resolution Hainan Island Regional Reanalysis (HNR) dataset and multi-source data, this study analyzes temperature changes on Hainan Island from 1900 to 2022, focusing on spatiotemporal trends, diurnal patterns, and probability distribution shifts. The findings reveal significant periodic temperature changes: weak warming (0.02–0.08 °C/decade) from 1900 to 1949, a temperature hiatus from 1950 to 1979, and accelerated warming (0.14–0.28 °C/decade) from 1979 to 2022. Coastal plains (0.11 °C/decade) warm faster than inland mountains (0.08 °C/decade), reflecting oceanic and topographic effects. Diurnal temperature variations show topographic dependence, with a maximum range (8–9 °C) in the north during the warm season, and a southwest–northeast gradient in the cold season. Probability density function analysis indicates that the curves for transitional and cold seasons show a noticeable widening and rightward shift, reflecting the increasing frequency of extreme temperature events under the trend of temperature rise. The study also finds that the occurrence time of daily maximum temperature over coastal plains is advancing (−0.05 to −0.1 h/decade). This study fills gaps in understanding tropical island climate responses under global warming and provides new insights into temperature changes over Hainan Island. Full article

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18 pages, 1108 KB

Open AccessArticle

Bridging Economic Development and Environmental Protection: Decomposition of CO₂ Emissions in a Romanian Context

by Mariana Carmelia Bălănică Dragomir, Carmen Gabriela Sîrbu, Gina Ioan and Ionel Sergiu Pîrju

Climate 2026, 14(1), 10; https://doi.org/10.3390/cli14010010 - 30 Dec 2025

Viewed by 390

Abstract

Climate change governance has become an essential concern for policymakers, with carbon dioxide (CO₂) emissions representing one of the most pressing challenges to sustainable economic development. In this context, understanding the main drivers of CO₂ emissions is essential for designing [...] Read more.

Climate change governance has become an essential concern for policymakers, with carbon dioxide (CO₂) emissions representing one of the most pressing challenges to sustainable economic development. In this context, understanding the main drivers of CO₂ emissions is essential for designing effective public policies that support Romania’s transition toward a low-carbon economy. This study investigates the determinants of CO₂ emissions in Romania’s energy sector between 2008 and 2023 using the Logarithmic Mean Divisia Index (LMDI) decomposition method. The analysis considers five key elements: the carbon intensity effect (ΔC), the energy mix effect (ΔM), the energy efficiency effect (ΔL), the economic effect (ΔB), and the population effect (ΔP). The results highlight the need for coherent governance frameworks and targeted policy measures to balance economic expansion with environmental sustainability. The study offers actionable insights for public authorities aiming to strengthen Romania’s climate governance and align national strategies with the objectives of the European Green Deal and climate neutrality by 2050. Full article

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18 pages, 3018 KB

Open AccessArticle

Different Climate Responses to Northern, Tropical, and Southern Volcanic Eruptions in CMIP6 Models

by Qinghong Zeng and Shengbo Chen

Climate 2026, 14(1), 8; https://doi.org/10.3390/cli14010008 - 28 Dec 2025

Viewed by 657

Abstract

Explosive volcanic eruptions are key drivers of climate variability, yet their hemispheric-dependent impacts remain uncertain. Using multi-model ensembles from Coupled Model Intercomparison Project Phase 6 (CMIP6) historical data and Decadal Climate Prediction Project (DCPP) simulations, this study examines how the spatial distribution of [...] Read more.

Explosive volcanic eruptions are key drivers of climate variability, yet their hemispheric-dependent impacts remain uncertain. Using multi-model ensembles from Coupled Model Intercomparison Project Phase 6 (CMIP6) historical data and Decadal Climate Prediction Project (DCPP) simulations, this study examines how the spatial distribution of volcanic aerosols modulates climate responses to Northern Hemisphere (NH), Tropical (TR), and Southern Hemisphere (SH) eruptions. The CMIP6 ensemble captures observed temperature and precipitation patterns, providing a robust basis for assessing volcanic effects. The results show that the hemispheric distribution of aerosols strongly controls radiative forcing, surface air temperature, and hydrological responses. TR eruptions cause nearly symmetric cooling and widespread tropical rainfall reduction, while NH and SH eruptions produce asymmetric temperature anomalies and clear Intertropical Convergence Zone (ITCZ) displacements away from the perturbed hemisphere. The vertical temperature structure, characterized by stratospheric warming and tropospheric cooling, further amplifies hemispheric contrasts through enhanced cross-equatorial energy transport and shifts in the Hadley circulation. ENSO-like responses depend on eruption latitude, TR and NH eruptions favor El Niño–like warming through westerly wind anomalies and Bjerknes feedback, and SH eruptions induce La Niña–like cooling. The DCPP experiments confirm that these signals primarily arise from volcanic forcing rather than internal variability. These findings highlight the critical role of aerosol asymmetry and vertical temperature structure in shaping post-eruption climate patterns and advancing the understanding of volcanic–climate interactions. Full article

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32 pages, 8478 KB

Open AccessArticle

Regionalization of Updated Intensity-Duration-Frequency Curves for Romania and the Consequences of Climate Change on Sub-Daily Rainfall

by Nicolai Sîrbu, Gabriel Racovițeanu and Radu Drobot

Climate 2026, 14(1), 7; https://doi.org/10.3390/cli14010007 - 27 Dec 2025

Viewed by 565

Abstract

Intensity–Duration–Frequency (IDF) curves are essential tools in the design of stormwater management systems and are often used over long periods without frequent updates. However, the continuous collection of rainfall data and the expansion of monitoring networks call for regular revisions of these curves. [...] Read more.

Intensity–Duration–Frequency (IDF) curves are essential tools in the design of stormwater management systems and are often used over long periods without frequent updates. However, the continuous collection of rainfall data and the expansion of monitoring networks call for regular revisions of these curves. In Romania, current engineering and hydrological practices still rely on regionalized IDF graphs developed in 1973. Given the ongoing effects of climate change—particularly the increased frequency and, more significantly, intensity of extreme rainfall events—updating these curves has become critical. Incorporating recent observations is essential not only for methodological accuracy but also to support climate-resilient infrastructure design. This study employs updated IDF curves provided by the National Administration of Meteorology, based on 30 years of precipitation records from 68 meteorological stations across Romania. The main objective is to evaluate alternative regionalization approaches—including clustering methods, geographic proximity analysis, and hourly precipitation isolines for a 1:10 Annual Exceedance Frequency—to develop a new regionalization model and the corresponding nationwide IDF relationships. A comparative analysis using raster-based regional rainfall datasets from both the 1973 and 2025 regionalizations revealed significant changes in precipitation patterns. Short-duration rainfall events (5, 10, and 30 min) have increased in intensity across most regions, while long-duration events (3, 6, 12, and 24 h) have generally decreased in magnitude in several areas. These findings highlight a growing trend toward more intense short-term convective storms, underlining the urgent need for improved flash flood prevention and urban stormwater management strategies. Full article

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10 pages, 6766 KB

Open AccessArticle

First Evidence into the Association of Warming with Stroke and Myocardial Infarction Mortality in the Brazilian Amazon

by Marcele Farias Silva Monteiro, Thainá Thamara Oliveira-Machado, Cícero Roniel, Gleyce Gabrielle Pereira Costa-Guimarães, Erick Augusto Oliveira-Machado, Diego Simeone and Aldemir B. Oliveira-Filho

Climate 2026, 14(1), 6; https://doi.org/10.3390/cli14010006 - 27 Dec 2025

Viewed by 516

Abstract

Rising temperatures intensify thermoregulatory stress, leading to increased cerebrovascular and cardiovascular morbidity and mortality. In this study, we aimed to examine the associations between rising temperatures and cerebrovascular and cardiovascular mortality in the Brazilian Amazon. Deaths from stroke and myocardial infarction were analyzed [...] Read more.

Rising temperatures intensify thermoregulatory stress, leading to increased cerebrovascular and cardiovascular morbidity and mortality. In this study, we aimed to examine the associations between rising temperatures and cerebrovascular and cardiovascular mortality in the Brazilian Amazon. Deaths from stroke and myocardial infarction were analyzed alongside monthly mean temperatures from 2000–2023. Poisson regression models were used to assess temporal trends and age-specific differences, whereas time series and distributed lag models were used to evaluate the influences of temperature and the cumulative effects of extreme heat. Increases in mean temperature were significantly associated with higher mortality for both outcomes, with older adults showing greater vulnerability, particularly those aged over 50 years. Prolonged exposure to extreme heat increased mortality risk, and the trend became more evident after 2015. These findings are the first to demonstrate that warming is associated with increased cerebrovascular and cardiovascular mortality in Amazonian populations, underscoring the urgent need for mitigation and adaptation strategies such as early warning systems, climate-resilient infrastructure, and improved healthcare access. Full article

(This article belongs to the Special Issue Climate Impact on Human Health)

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13 pages, 3486 KB

Open AccessArticle

Deep Diving into the “Post 1.5 °C Climate” Heatwave Events in Ouagadougou During Spring 2024

by Wendkuni Ghislain Noba, Dazangwende Emmanuel Poan, Kiswendsida Hyacinth Guigma, Martha Marie Vogel and Thomas Rakiswende Béré

Climate 2026, 14(1), 5; https://doi.org/10.3390/cli14010005 - 25 Dec 2025

Viewed by 824

Abstract

The West African Sahel suffered an unprecedented hot season during spring 2024 especially marked by noticeable heatwave episodes in the urban context of Burkina Faso’s capital, Ouagadougou, where significant impacts were reported. These heat events are analyzed to link hazards with impacts and [...] Read more.

The West African Sahel suffered an unprecedented hot season during spring 2024 especially marked by noticeable heatwave episodes in the urban context of Burkina Faso’s capital, Ouagadougou, where significant impacts were reported. These heat events are analyzed to link hazards with impacts and improve early warning systems in the under-recognized Sahel context. Using observational data from the Burkina Faso National Meteorological Agency and the European reanalysis, ERA5, anomalies of both daily maximum (Tmax) and minimum (Tmin) temperatures were analyzed. The results show that, during the first half of 2024, monthly Tmax and Tmin anomalies were highly positive compared to the reference period 1991–2020. A total of four daytime and one nighttime heatwave events were detected. The longest daytime heatwave lasted six days with observed Tmax reaching 44.5 °C. The unique nighttime heatwave was at least twice as long as the longest daytime heatwave, persisting 13 days between late April and early May. In addition, the heat was not evenly distributed spatially as some districts were significantly hotter than the rest of the city, suggesting possible urban/local effects. These results underscore the occurrence of exceptional heat in 2024 and the need for efforts towards heatwave risk mapping and management in African cities. Full article

(This article belongs to the Section Weather, Events and Impacts)

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27 pages, 322 KB

Open AccessArticle

What Difference Can a Workshop Make? Lessons from an Evaluation of Eight Place-Based Climate Adaptation Workshops in the United States

by Marc J. Stern, Jennifer J. Brousseau and Caleb O’Brien

Climate 2026, 14(1), 4; https://doi.org/10.3390/cli14010004 - 24 Dec 2025

Viewed by 513

Abstract

Place-based climate adaptation workshops are designed to help communities understand their climate-related vulnerabilities and plan adaptive actions in response. Through a series of surveys and interviews with participants, we examined the immediate and long-term impacts of eight place-based climate adaptation workshops in the [...] Read more.

Place-based climate adaptation workshops are designed to help communities understand their climate-related vulnerabilities and plan adaptive actions in response. Through a series of surveys and interviews with participants, we examined the immediate and long-term impacts of eight place-based climate adaptation workshops in the United States. Six took place online due to COVID-19 restrictions; two took place in-person. All workshops positively enhanced declarative, procedural, and relational knowledge of participants and, to a lesser extent, their personal commitment to work on climate adaptation, optimism about climate adaptation in their communities, and perceptions of qualities of the network of actors engaged locally in climate adaptation. In-person workshops yielded somewhat stronger positive influences on relationship-building than online workshops. Most participants who responded to surveys 6 months to a year after the workshop reported that their workshop had a “minor” to “moderate” impact on stimulating meaningful adaptation actions in their area. Reported actions attributed to the workshops included the incorporation of climate adaptation into formal planning documents, the expansion of adaptation outreach, consideration of climate adaptation in day-to-day planning and decision-making in local government departments, and both successful and unsuccessful grant applications for projects and positions associated with climate adaptation. We describe the workshops’ design, as well as participant assessments of the value of different workshop components. We conclude with lessons learned for future effective workshop planning and design. Full article

(This article belongs to the Collection Adaptation and Mitigation Practices and Frameworks)

35 pages, 1902 KB

Open AccessReview

Recent Advancements and Challenges in Artificial Intelligence for Digital Twins of the Ocean

by Vassiliki Metheniti, Antonios Parasyris, Ricardo Santos Pereira and Garabet Kazanjian

Climate 2026, 14(1), 3; https://doi.org/10.3390/cli14010003 - 23 Dec 2025

Viewed by 747

Abstract

The Digital Twins of the Ocean (DTOs) represent an emerging framework for monitoring, simulating, and predicting ocean dynamics, supporting a range of applications relevant to understanding and responding to the global climate system. By integrating large-scale, multi-sourced datasets with advanced numerical models, DTOs [...] Read more.

The Digital Twins of the Ocean (DTOs) represent an emerging framework for monitoring, simulating, and predicting ocean dynamics, supporting a range of applications relevant to understanding and responding to the global climate system. By integrating large-scale, multi-sourced datasets with advanced numerical models, DTOs provide a powerful tool for climate science. This review examines the role of machine learning (ML) in advancing DTOs applications, addressing the limitations of traditional methodologies under current conditions of increasing data availability from satellites, in situ sensors, and high-resolution numerical models. We highlight how ML serves as a versatile tool for enhancing DTOs capabilities, including real-time forecasting, correcting model biases, and filling data gaps where conventional approaches fall short. Furthermore, we review surrogate models that aim to complement or replace traditional physical models, offering increasing accuracy and the appeal of much faster inference for forecasts, and the insertion of hybrid models, which couple physics-based simulations with ML algorithms and are proving to be continuously improving in accuracy for complex oceanographic tasks as bigger datasets become available and methodologies evolve. This paper provides a comprehensive review of ML applications within DTOs, focusing on key areas such as water quality and marine biodiversity, ports, marine pollution, fisheries, and renewable energy. The review concludes with a discussion of future research directions and the potential of ML to foster more robust and practical DTOs, ultimately supporting informed decision-making for sustainable ocean management. Full article

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22 pages, 6315 KB

Open AccessArticle

Intensification of SUHI During Extreme Heat Events: An Eight-Year Summer Analysis for Lecce (2018–2025)

by Antonio Esposito, Riccardo Buccolieri, Jose Luis Santiago and Gianluca Pappaccogli

Climate 2026, 14(1), 2; https://doi.org/10.3390/cli14010002 - 22 Dec 2025

Viewed by 1098

Abstract

The effects of extreme heat events on Surface Urban Heat Island Intensity (SUHII) were investigated in Lecce (southern Italy) during the summer months (June–August) from 2018 to 2025. The analysis began with the identification of heatwave frequency, duration, and intensity using the Warm [...] Read more.

The effects of extreme heat events on Surface Urban Heat Island Intensity (SUHII) were investigated in Lecce (southern Italy) during the summer months (June–August) from 2018 to 2025. The analysis began with the identification of heatwave frequency, duration, and intensity using the Warm Spell Duration Index (WSDI), based on a homogenized long-term temperature record, which indicated a progressive increase in persistent extreme events in recent years. High-resolution ECOSTRESS land surface temperature (LST) data were then processed and combined with CORINE Land Cover (CLC) information to examine the thermal response of different urban fabrics, compact residential areas, continuous/discontinuous urban fabric, and industrial–commercial zones. SUHII was derived from each ECOSTRESS acquisition and evaluated across multiple diurnal intervals to assess temporal variability under both normal and WSDI conditions. The results show a consistent diurnal asymmetry: daytime SUHII becomes more negative during WSDI periods, reflecting enhanced rural warming under dry and highly irradiated conditions, despite overall higher absolute LST during heatwaves, whereas nighttime SUHII intensifies, particularly in dense urban areas where higher thermal inertia promotes persistent heat retention. Statistical analyses confirm significant differences between normal and extreme conditions across all classes and time intervals. These findings demonstrate that extreme heat events alter the urban–rural thermal contrast by amplifying nighttime heat accumulation and reinforcing daytime negative SUHII values. The integration of WSDI-derived heatwave characterization with multi-year ECOSTRESS observations highlights the increasing thermal vulnerability of compact urban environments under intensifying summer extremes. Full article

(This article belongs to the Section Sustainable Urban Futures in a Changing Climate)

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16 pages, 1434 KB

Open AccessArticle

Estimation of Surface PM_2.5 Concentration from Satellite Aerosol Optical Depth Using a Constrained Observation-Based Model

by Olusegun G. Fawole, Samuel T. Ogunjo, Ayomide Olabode, Wumi Alabi and Rabia S. Sa’id

Climate 2026, 14(1), 1; https://doi.org/10.3390/cli14010001 - 22 Dec 2025

Viewed by 602

Abstract

Studies have established that extreme air pollution is more prevalent and is responsible for more deaths and disability-adjusted life years (DALY) in urban cities, especially in developing economies. However, the paucity of ground-based observation has greatly hindered extensive and long-term monitoring and, as [...] Read more.

Studies have established that extreme air pollution is more prevalent and is responsible for more deaths and disability-adjusted life years (DALY) in urban cities, especially in developing economies. However, the paucity of ground-based observation has greatly hindered extensive and long-term monitoring and, as such, a good understanding of the trend and characteristics of air quality where it matters most. Aerosol optical depth (AOD) from satellites retrievals provides good spatial and temporal resolutions of atmospheric aerosols and could be a good proxy for ground-level PM_2.5 concentration. This study used a Bayesian regression model to determine the parameters of a PM_2.5 model at four monitoring stations using AOD and selected atmospheric variables (PBLH and RH) as input. The dry-air reference value (K) and the integrated humidity coefficient (γ) were used to delineate the effects of the aerosol characteristics. The values of K and γ,

0.02 < K < 0.07

(

m^{2} g^{−1}

) and

0.54 < γ < 3.14

, respectively, are site-specific even within the same country as is the case for Lekki and Benin (both in Nigeria). The PM_2.5 estimates from the developed observation-based model were in good agreement with the ground-based observations (

0.55 < r < 0.77

). RH and a combination of PBLH-RH were the best performers in the development of the model. Firstly, this study identifies the unique range of values for K and

γ

for site-classes in the sub-Saharan tropical climate. Secondly, PBLH adds more explanatory power to the PM_2.5 estimates in Benin and Douala (both non-coastal cities) while RH improves the performance of the model significantly in Lekki and Owendo (both coastal cities). For West Africa and similar data-sparse regions, the methodology presented here offers a practical pathway to enhance air quality monitoring capabilities. Full article

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