Journal Description
Climate
Climate
is a scientific, peer-reviewed, open access journal of climate science published online monthly by MDPI. The American Society of Adaptation Professionals (ASAP) is affiliated with Climate and its members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), GeoRef, AGRIS, and other databases.
- Journal Rank: JCR - Q2 (Meteorology and Atmospheric Sciences) / CiteScore - Q2 (Atmospheric Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.9 days after submission; acceptance to publication is undertaken in 3.8 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.0 (2023);
5-Year Impact Factor:
3.3 (2023)
Latest Articles
Assessing NOAA/GFDL Models Performance for South American Seasonal Climate: Insights from CMIP6 Historical Runs and Future Projections
Climate 2025, 13(1), 4; https://doi.org/10.3390/cli13010004 (registering DOI) - 28 Dec 2024
Abstract
Climate prediction is of fundamental importance to various sectors of society and the economy, as it can predict the likelihood of droughts or excessive rainfall in vulnerable regions. Climate models are useful tools in producing reliable climate forecasts, which have become increasingly vital
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Climate prediction is of fundamental importance to various sectors of society and the economy, as it can predict the likelihood of droughts or excessive rainfall in vulnerable regions. Climate models are useful tools in producing reliable climate forecasts, which have become increasingly vital due to the rising impacts of climate change. As global temperatures rise, changes in precipitation patterns are expected, increasing the importance of reliable seasonal forecasts to support planning and adaptation efforts. In this study, we evaluated the performance of NOAA/GFDL models from CMIP6 simulations in representing the climate of South America under three configurations: atmosphere-only, coupled ocean-atmosphere, and Earth system. Our analysis revealed that all three configurations successfully captured key climatic features, such as the South Atlantic Convergence Zone (SACZ), the Bolivian High, and the Intertropical Convergence Zone (ITCZ). However, coupled models exhibited larger errors and lower correlation (below 0.6), particularly over the ocean and the South American Monsoon System, which indicates a poor representation of precipitation compared with atmospheric models. The coupled models also overestimated upward motion linked to the southern Hadley cell during austral summer and underestimated it during winter, whereas the atmosphere-only models more accurately simulated the Walker circulation, showing stronger vertical motion around the Amazon. In contrast, the coupled models simulated stronger upward motion over Northeast Brazil, which is inconsistent with reanalysis data. Moreover, we provided insights into how model biases may evolve under climate change scenarios. Future climate projections for the mid-century period (2030–2060) under the SSP2-4.5 and SSP5-8.5 scenarios indicate significant changes in the global energy balance, with an increase of up to 0.9 W/m2. Additionally, the projections reveal significant warming and drying in most of the continent, particularly during the austral spring, accompanied by increases in sensible heat flux and decreases in latent heat flux. These findings highlight the risk of severe and prolonged droughts in some regions and intensified rainfall in others. By identifying and quantifying the biases inherent in climate models, this study provides insights to enhance seasonal forecasts in South America, ultimately supporting strategic planning, impact assessments, and adaptation strategies in vulnerable regions.
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(This article belongs to the Section Climate Dynamics and Modelling)
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Agro-Climatic Zoning of the Territory of Northern Kazakhstan for Zoning of Agricultural Crops Under Conditions of Climate Change
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Saken Baisholanov, Kanat Akshalov, Yerbolat Mukanov, Bakytbek Zhumabek and Ergali Karakulov
Climate 2025, 13(1), 3; https://doi.org/10.3390/cli13010003 (registering DOI) - 28 Dec 2024
Abstract
Assessments of the agro-climatic resources of Northern Kazakhstan are urgently needed in the face of climate change and increasing threats to food security in the world, and they can provide valuable information for specialists in the field of agriculture. To assess the agro-climatic
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Assessments of the agro-climatic resources of Northern Kazakhstan are urgently needed in the face of climate change and increasing threats to food security in the world, and they can provide valuable information for specialists in the field of agriculture. To assess the agro-climatic conditions of Northern Kazakhstan, the following agro-climatic indices were used: heat availability, moisture availability, and aridity of the growing season for the period 1991–2023. The research results rendered it possible to build maps of the spatial distribution of agro-climatic indicators, and five agro-climatic zones, ranging from “moderately humid moderately warm” in the north to “very arid moderately hot” in the south of Northern Kazakhstan, were identified. Recommendations were developed with respect to the agro-climatic zoning of main crops, taking into account the climatic resources of Northern Kazakhstan. The data obtained will be used for the strategic planning of the agricultural crop industry in Northern Kazakhstan.
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(This article belongs to the Section Climate Adaptation and Mitigation)
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User-Driven Climate Resilience Across Southern European Regions
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Georgios Xekalakis, Patricia Molina Lopez, Manuel Argamasilla Ruiz, Tanja Tötzer, Patrick Kaleta, Konstantinos Karystinakis, Anastasia Moumtzidou, Renata Forjan, Petros Christou, Christos Anastasiou, Venera Pavone, Gigliola D’Angelo, Francisco Solano Cobos, Marianne Bügelmayer-Blaschek, Socrates Boutsis, Marija Vurnek, Ivan Murano, Paola Del Prete, Peter Kutschera, Dimitrios Leonidis, Evi Kazamia, Adam Warde, James Hawkes, Pietro Colonna, Vincenzo Petruso, Beniamino Russo, Mattia Federico Leone, Martin Schneider, Andrea Hochebner, Giulio Zuccaro and Denis Havlikadd
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Climate 2025, 13(1), 2; https://doi.org/10.3390/cli13010002 (registering DOI) - 27 Dec 2024
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This study presents the ClimEmpower framework, a user-driven approach to enhancing climate resilience across five climate-vulnerable regions in Southern Europe: Costa del Sol (Spain), Central Greece, the Troodos Mountains (Cyprus), Osijek-Baranja County (Croatia), and Sicily (Italy). The project employs a region-specific methodology that
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This study presents the ClimEmpower framework, a user-driven approach to enhancing climate resilience across five climate-vulnerable regions in Southern Europe: Costa del Sol (Spain), Central Greece, the Troodos Mountains (Cyprus), Osijek-Baranja County (Croatia), and Sicily (Italy). The project employs a region-specific methodology that integrates climate risk assessments, stakeholder engagement through Communities of Practice (CoPs), and the development of innovative climate services tailored to local needs. These regions, characterized by unique environmental and socio-economic vulnerabilities, face shared hazards such as droughts, heatwaves, and floods, alongside region-specific challenges like salinization and biodiversity loss. ClimEmpower identifies critical gaps in high-resolution data, cross-sectoral collaboration, and capacity-building efforts, underscoring barriers to effective adaptation. This work aims to provide a foundational resource, offering a comprehensive overview of the current situation, including needs, gaps, priorities, and expectations across the target regions. By establishing this baseline, it facilitates future research and comparative analyses, contributing to the development of robust, region-specific resilience strategies. The ClimEmpower framework offers scalable and replicable solutions aligned with the European Green Deal’s climate resilience goals, advancing adaptation planning and providing actionable insights for broader European initiatives.
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Changes in Climate and Their Implications for Cattle Nutrition and Management
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Bashiri Iddy Muzzo, R. Douglas Ramsey and Juan J. Villalba
Climate 2025, 13(1), 1; https://doi.org/10.3390/cli13010001 - 24 Dec 2024
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Climate change is a global challenge that impacts rangeland and pastureland landscapes by inducing shifts in temperature variability, precipitation patterns, and extreme weather events. These changes alter soil and plant conditions, reducing forage availability and chemical composition and leading to nutritional stress in
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Climate change is a global challenge that impacts rangeland and pastureland landscapes by inducing shifts in temperature variability, precipitation patterns, and extreme weather events. These changes alter soil and plant conditions, reducing forage availability and chemical composition and leading to nutritional stress in cattle. This stress occurs when animals lack adequate water and feed sources or when these resources are insufficient in quantity, composition, or nutrient balance. Several strategies are essential to address these impacts. Genetic selection, epigenetic biomarkers, and exploration of epigenetic memories present promising avenues for enhancing the resilience of cattle populations and improving adaptation to environmental stresses. Remote sensing and GIS technologies assist in locating wet spots to establish islands of plant diversity and high forage quality for grazing amid ongoing climate change challenges. Establishing islands of functional plant diversity improves forage quality, reduces carbon and nitrogen footprints, and provides essential nutrients and bioactives, thus enhancing cattle health, welfare, and productivity. Real-time GPS collars coupled with accelerometers provide detailed data on cattle movement and activity, aiding livestock nutrition management while mitigating heat stress. Integrating these strategies may offer significant advantages to animals facing a changing world while securing the future of livestock production and the global food system.
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(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)
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The Impact of Climate Change on Energy Consumption on Small Tropical Islands
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Julien Gargani
Climate 2024, 12(12), 227; https://doi.org/10.3390/cli12120227 - 23 Dec 2024
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The anthropic causes of climate change are well known, but the influence of climate change on society needs to be better estimated. This study estimates the impact of climate change on energy consumption on small tropical islands using monthly temperatures and energy production/consumption
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The anthropic causes of climate change are well known, but the influence of climate change on society needs to be better estimated. This study estimates the impact of climate change on energy consumption on small tropical islands using monthly temperatures and energy production/consumption statistics during the last decades. Here, we show, using energy, meteorological, demographic, and economic datasets, as well as statistical correlations, that energy consumption is sensitive to (i) cyclonic activity and (ii) temperature warming. On small tropical islands, increased electricity consumption correlates with temperatures rising above 26 °C in relation to air conditioner electricity consumption. On La Réunion Island, a +1 °C increase is expected to cause an electricity production of 1.5 MWh/inhabitant per year, representing a growth of 3.2%. Considering that non-renewable sources are primarily used to produce electricity, this feedback contributed significantly (i.e., 2000 to 4000 TWh) to the greenhouse gas increase caused by climate warming over the last decades on tropical islands. Demographic and wealth variations, as well as socio-economic crises, also have a significant impact on energy consumption (2 kWh for 1000 inhabitants, 0.008 GWh/inhabitant growth for a 10,000 GDP/inhabitant growth, and a 0.2 GWh/inhabitant decrease during COVID-19, for annual consumption, respectively) and must be taken into account for decadal variation analysis. The relationship between climate change and energy consumption in tropical areas should be better integrated into climatic scenarios to adapt building isolation and energy production.
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A Merging Approach for Improving the Quality of Gridded Precipitation Datasets over Burkina Faso
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Moussa Waongo, Juste Nabassebeguelogo Garba, Ulrich Jacques Diasso, Windmanagda Sawadogo, Wendyam Lazare Sawadogo and Tizane Daho
Climate 2024, 12(12), 226; https://doi.org/10.3390/cli12120226 - 20 Dec 2024
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Satellite precipitation estimates are crucial for managing climate-related risks such as droughts and floods. However, these datasets often contain systematic errors due to the observation methods used. The accuracy of these estimates can be enhanced by integrating spatial and temporal resolution data from
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Satellite precipitation estimates are crucial for managing climate-related risks such as droughts and floods. However, these datasets often contain systematic errors due to the observation methods used. The accuracy of these estimates can be enhanced by integrating spatial and temporal resolution data from in situ observations. Nevertheless, the accuracy of the merged dataset is influenced by the density and distribution of rain gauges, which can vary regionally. This paper presents an approach to improve satellite precipitation data (SPD) over Burkina Faso. Two bias correction methods, Empirical Quantile Mapping (EQM) and Time and Space-Variant (TSV), have been applied to the SPD to yield a bias-corrected dataset for the period 1991–2020. The most accurate bias-corrected dataset is then combined with in situ observations using the Regression Kriging (RK) method to produce a merged precipitation dataset. The findings show that both bias correction methods achieve similar reductions in RMS error, with higher correlation coefficients (approximately 0.8–0.9) and a normalized standard deviation closer to 1. However, EQM generally demonstrates more robust and consistent performance, particularly in terms of correlation and RMS error reduction. On a monthly scale, the superiority of EQM is most evident in June, September, and October. Following the merging process, the final dataset, which incorporates satellite information in addition to in situ observations, demonstrates higher performance. It shows improvements in the coefficient of determination by 83%, bias by 11.4%, mean error by 96.7%, and root-mean-square error by 95.5%. The operational implementation of this approach provides substantial support for decision-making in regions heavily reliant on rainfed agriculture and sensitive to climate variability. Delivering more precise and reliable precipitation datasets enables more informed decisions and significantly enhances policy-making processes in the agricultural and water resources sectors of Burkina Faso.
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Effect of Temperature on the Spread of Contagious Diseases: Evidence from over 2000 Years of Data
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Mehmet Balcilar, Zinnia Mukherjee, Rangan Gupta and Sonali Das
Climate 2024, 12(12), 225; https://doi.org/10.3390/cli12120225 - 20 Dec 2024
Abstract
The COVID-19 pandemic led to a surge in interest among scholars and public health professionals in identifying the predictors of health shocks and their transmission in the population. With temperature increases becoming a persistent climate stress, our aim is to evaluate how temperature
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The COVID-19 pandemic led to a surge in interest among scholars and public health professionals in identifying the predictors of health shocks and their transmission in the population. With temperature increases becoming a persistent climate stress, our aim is to evaluate how temperature specifically impacts the incidences of contagious disease. Using annual data from 1 AD to 2021 AD on the incidence of contagious disease and temperature anomalies, we apply both parametric and nonparametric modelling techniques and provide estimates of the contemporaneous, as well as lagged, effects of temperature anomalies on the spread of contagious diseases. A nonhomogeneous hidden Markov model is then applied to estimate the time-varying transition probabilities between hidden states where the transition probabilities are governed by covariates. For all empirical specifications, we find consistent evidence that temperature anomalies have a statistically significant effect on the incidence of a contagious disease in any given year covered in the sample period. The best fit model further indicates that the contemporaneous effect of a temperature anomaly on the response variable is the strongest. As temperature predictions continue to become more accurate, our results indicate that such information can be used to implement effective public health responses to limit the spread of contagious diseases. These findings further have implications for designing cost effective infectious disease control policies for different regions of the world.
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(This article belongs to the Special Issue Climate Impact on Human Health)
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Development of a Diagnostic Algorithm for Detecting Freezing Precipitation from ERA5 Dataset: An Adjustment to the Far East
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Mikhail Pichugin, Irina Gurvich, Anastasiya Baranyuk, Vladimir Kuleshov and Elena Khazanova
Climate 2024, 12(12), 224; https://doi.org/10.3390/cli12120224 - 17 Dec 2024
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Freezing precipitation and the resultant ice glaze can have catastrophic impacts on urban infrastructure, the environment, forests, and various industries, including transportation, energy, and agriculture. In this study, we develop and evaluate regional algorithms for detecting freezing precipitations in the Far East, utilizing
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Freezing precipitation and the resultant ice glaze can have catastrophic impacts on urban infrastructure, the environment, forests, and various industries, including transportation, energy, and agriculture. In this study, we develop and evaluate regional algorithms for detecting freezing precipitations in the Far East, utilizing the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, along with standard meteorological observations for 20 cold seasons (September–May) from 2004 to 2024. We propose modified diagnostic algorithms based on vertical atmospheric temperature and humidity profiles, as well as near-surface characteristics. Additionally, we apply a majority voting ensemble (MVE) technique to integrate outputs from multiple algorithms, thereby enhancing classification accuracy. Evaluation of detection skills shows significant improvements over the original method developed at the Finnish Meteorological Institute and the ERA5 precipitation-type product. The MVE-based method demonstrates optimal verification statistics. Furthermore, the modified algorithms validly reproduce the spatially averaged inter-annual variability of freezing precipitation activity in both continental (mean correlation of 0.93) and island (correlation of 0.54) regions. Overall, our findings offer a more effective and valuable tool for operational activities and climatological assessments in the Far East.
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(This article belongs to the Special Issue Extreme Weather Detection, Attribution and Adaptation Design)
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Mapping Methane—The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning
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Hanqing Bi and Suresh Neethirajan
Climate 2024, 12(12), 223; https://doi.org/10.3390/cli12120223 - 15 Dec 2024
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Methane emissions from dairy farms are a significant driver of climate change, yet their relationship with farm-specific practices remains poorly understood. This study employs Sentinel-5P satellite-derived methane column concentrations as a proxy to examine emission dynamics across 11 dairy farms in Eastern Canada,
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Methane emissions from dairy farms are a significant driver of climate change, yet their relationship with farm-specific practices remains poorly understood. This study employs Sentinel-5P satellite-derived methane column concentrations as a proxy to examine emission dynamics across 11 dairy farms in Eastern Canada, using data collected between January 2020 and December 2022. By integrating advanced analytics, we identified key drivers of methane concentrations, including herd genetics, feeding practices, and management strategies. Statistical tools such as Variance Inflation Factor (VIF) and Principal Component Analysis (PCA) addressed multicollinearity, stabilizing predictive models. Machine learning approaches—Random Forest and Neural Networks—revealed a strong negative correlation between methane concentrations and the Estimated Breeding Value (EBV) for protein percentage, demonstrating the potential of genetic selection for emissions mitigation. Our approach refined concentration estimates by integrating satellite data with localized atmospheric modeling, enhancing accuracy and spatial resolution. These findings highlight the transformative potential of combining satellite observations, machine learning, and farm-level characteristics to advance sustainable dairy farming. This research underscores the importance of targeted breeding programs and management strategies to optimize environmental and economic outcomes. Future work should expand datasets and apply inversion modeling for finer-scale emission quantification, advancing scalable solutions that balance productivity with ecological sustainability.
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(This article belongs to the Special Issue Applications of Smart Technologies in Climate Risk and Adaptation)
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“Taking Action in Community Is Much, Much Preferable to Doing It Alone”: An Examination of Multi-Level Facilitators of and Barriers to Sustained Collective Climate Change Activism Among US Residents
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Lauren Dayton, Kelsie Parker, Julia Ross, Saraniya Tharmarajah and Carl Latkin
Climate 2024, 12(12), 222; https://doi.org/10.3390/cli12120222 - 14 Dec 2024
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To enact climate mitigation policies, sustained collective activism is essential to create political pressure and prioritize addressing climate change. Climate change activism includes behaviors such as contacting elected officials to urge them to take action on climate change, volunteering, and signing petitions. Climate
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To enact climate mitigation policies, sustained collective activism is essential to create political pressure and prioritize addressing climate change. Climate change activism includes behaviors such as contacting elected officials to urge them to take action on climate change, volunteering, and signing petitions. Climate change activism is often measured as a one-time event, not sustained activism efforts, which are necessary to enact sufficiently impactful policy changes. To examine barriers to and facilitators of sustained climate change activism, 23 in-depth interviews were conducted between August and December 2023 among members of an innovative national climate change-focused organization. Eligibility included being at least 18 years of age, English-speaking, a US resident, and highly engaged in a climate change activism group. Content analysis of interview transcripts was employed, and five themes emerged as barriers, four themes as facilitators, and five themes as both facilitators of and barriers to sustained climate change activism. The study identified strategies to promote the critical behavior of sustained climate change activism, which included fostering a community of climate change activists, clear instructions on how to engage in activism behaviors for all technical abilities, supporting mental health, and creating climate change activism as a habit and identity.
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The Drought Regime in Southern Africa: Long-Term Space-Time Distribution of Main Drought Descriptors
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Fernando Maliti Chivangulula, Malik Amraoui and Mário Gonzalez Pereira
Climate 2024, 12(12), 221; https://doi.org/10.3390/cli12120221 - 13 Dec 2024
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Drought consequences depend on its type and class and on the preparedness and resistance of communities, which, in turn, depends on the knowledge and capacity to manage this climate disturbance. Therefore, this study aims to assess the drought regime in Southern Africa based
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Drought consequences depend on its type and class and on the preparedness and resistance of communities, which, in turn, depends on the knowledge and capacity to manage this climate disturbance. Therefore, this study aims to assess the drought regime in Southern Africa based on vegetation and meteorological indices. The SPI and SPEI were calculated at different timescales, using ERA5 data for the 1971–2020 period. The results revealed the following: (i) droughts of various classes at different timescales occurred throughout the study period and region; (ii) a greater Sum of Drought Intensity and Number, in all classes, but lower duration and severity of droughts with the SPI than with the SPEI; (iii) drought frequency varies from 1.3 droughts/decade to 4.5 droughts/decade, for the SPI at 12- to 3-month timescales; (iv) the number, duration, severity and intensity of drought present high spatial variability, which tends to decrease with the increasing timescale; (v) the area affected by drought increased, on average, 6.6%/decade with the SPI and 9.1%/decade with the SPEI; and (vi) a high spatial-temporal agreement between drought and vegetation indices that confirm the dryness of vegetation during drought. These results aim to support policymakers and managers in defining legislation and strategies to manage drought and water resources.
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A Comprehensive AI Approach for Monitoring and Forecasting Medicanes Development
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Javier Martinez-Amaya, Veronica Nieves and Jordi Muñoz-Mari
Climate 2024, 12(12), 220; https://doi.org/10.3390/cli12120220 - 13 Dec 2024
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Medicanes are rare cyclones in the Mediterranean Sea, with intensifying trends partly attributed to climate change. Despite progress, challenges persist in understanding and predicting these storms due to limited historical tracking data and their infrequent occurrence, which make monitoring and forecasting difficult. In
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Medicanes are rare cyclones in the Mediterranean Sea, with intensifying trends partly attributed to climate change. Despite progress, challenges persist in understanding and predicting these storms due to limited historical tracking data and their infrequent occurrence, which make monitoring and forecasting difficult. In response to this issue, we present an AI-based system for tracking and forecasting Medicanes, employing machine learning techniques to identify cyclone positions and key evolving spatio-temporal structural features of the cloud system that are associated with their intensification and potential extreme development. While the forecasting model currently operates with limited training data, it can predict extreme Medicane events up to two days in advance, with precision rates ranging from 65% to 80%. These innovative data-driven methods for tracking and forecasting provide a foundation for refining AI models and enhancing our ability to respond effectively to such events.
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(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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Analysis of the Observed Trends in Rainfall and Temperature Patterns in North-Eastern Nigeria
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Deborah Ishaku, Emmanuel Tanko Umaru, Abel Aderemi Adebayo, Ralf Löwner and Appollonia Aimiosino Okhimamhe
Climate 2024, 12(12), 219; https://doi.org/10.3390/cli12120219 - 11 Dec 2024
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The present study offers a comprehensive evaluation of the monthly rainfall and temperature patterns across nine stations and fifty-nine points in North-Eastern Nigeria using NASA’s Prediction of Worldwide Energy Resources data, spanning four decades (1981–2021). By employing the Mann–Kendall (MK) test and inverse
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The present study offers a comprehensive evaluation of the monthly rainfall and temperature patterns across nine stations and fifty-nine points in North-Eastern Nigeria using NASA’s Prediction of Worldwide Energy Resources data, spanning four decades (1981–2021). By employing the Mann–Kendall (MK) test and inverse distance weighting (IDW) interpolation, the researchers effectively detected and visualized trends in climate variables. The MK test results indicate contrasting rainfall trends, with notable decreases in Akko, Billiri, Maiduguri, Numan, and Yola, and increases in Gombe, Abadam, Biu, and Mubi. The trends in the maximum temperature were found to be statistically significant across all stations, showing a consistent increase, whereas the minimum temperature trends exhibited a slight but insignificant decrease. The application of the Theil–Sen slope estimator quantified these trends, providing nuanced insights into the magnitudes of changes in climate variables. The IDW results further corroborate the general trend of decreasing rainfall (z = −0.442), modest increases in the maximum temperature (z = 0.046), and a marginal decline in the minimum temperature (z = −0.005). This study makes an important contribution by advocating for the proactive dissemination of climate information. Given the evident climate shifts, particularly the increasing temperatures and fluctuating rainfall patterns, timely access to such information is crucial to enhancing climate resilience in the region. The rigorous statistical methods applied and the detailed spatial analysis strengthen the validity of these findings, making this study a valuable resource for both researchers and policymakers aiming to address climate variability in North-Eastern Nigeria. These research results may also be useful for understanding the climate variabilities in different parts of the world.
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Multi-Secular Trend of Drought Indices in Padua, Italy
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Francesca Becherini, Claudio Stefanini, Antonio della Valle, Francesco Rech, Fabio Zecchini and Dario Camuffo
Climate 2024, 12(12), 218; https://doi.org/10.3390/cli12120218 - 10 Dec 2024
Abstract
The aim of this work is to investigate drought variability in Padua, northern Italy, over a nearly 300-year period, from 1725 to 2023. Two well-established and widely used indices are calculated, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI).
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The aim of this work is to investigate drought variability in Padua, northern Italy, over a nearly 300-year period, from 1725 to 2023. Two well-established and widely used indices are calculated, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). They are compatible with a data series starting in the early instrumental period, as both can be estimated using only temperature and precipitation data. The Padua daily precipitation and temperature series from the early 18th century, which were recovered and homogenized with current observations, are used as datasets. The standard approach to estimate SPI and SPEI based on gamma and log-logistic probability distribution functions, respectively, is questioned, assessing the fitting performance of different distributions applied to monthly precipitation data. The best-performing distributions are identified for each index and accumulation period at annual and monthly scales, and their normality is evaluated. In general, they detect more extreme drought events than the standard functions. Moreover, the main statistical values of SPI are very similar, regardless of the approach type, as opposed to SPEI. The difference between SPI and SPEI time series calculated with the best-fit approach has increased since the mid-20th century, in particular in spring and summer, and can be related to ongoing global warming, which SPEI takes into account. The innovative trend analysis applied to SPEI12 indicates a general increasing trend in droughts, while for SPI12, it is significant only for severe events. Summer and fall are the most affected seasons. The critical drought intensity–duration–frequency curves provide an easily understandable relationship between the intensity, duration and frequency of the most severe droughts and allow for the calculation of return periods for the critical events of a certain duration. Moreover, the longest and most severe droughts over the 1725–2023 period are identified.
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(This article belongs to the Special Issue Climate Variability in the Mediterranean Region)
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Factors Contributing to Effective Climate Change Adaptation Projects in Water Management: Implications from the Developing Countries
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Yuki Shiga and Rajib Shaw
Climate 2024, 12(12), 217; https://doi.org/10.3390/cli12120217 - 10 Dec 2024
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The adaptation finance gap is widening as the impact of climate change grows more disruptive around the globe. Although progress in adaptation planning and implementation has been observed across all sectors and regions, this trend of a widening resource gap calls for more
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The adaptation finance gap is widening as the impact of climate change grows more disruptive around the globe. Although progress in adaptation planning and implementation has been observed across all sectors and regions, this trend of a widening resource gap calls for more ‘effective’ climate adaptation projects. Therefore, the purpose of this paper is to provide a comprehensive analysis to explore potential factors contributing to the effectiveness of climate change projects in developing countries with a particular focus on water management financed under multilateral funds that have been implemented on the ground, completed and documented. Thirty-five projects from the multilateral funds were collected and analyzed for this purpose. Project evaluation documents have been studied, and the effectiveness rating at completion has been assessed against possible contributing factors through regression analysis. The results showed that the factors contributing to project effectiveness converge around several elements: (i) capacity building and education (|r| > 0.3); (ii) healthy and resilient livelihoods (|r| > 0.2); and (iii) climate data and a robust theory of change (stated by >30% of projects). The implications from this study can provide a useful quantitative ground for discussion around the effective adaptation projects in water management as well as inform relevant international processes such as the Global Goal on Adaptation and global stocktake.
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Views of Health Professionals About Climate and Health in Sierra Leone: A Cross-Sectional Study
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Isaac S. Sesay and Konstantinos C. Makris
Climate 2024, 12(12), 216; https://doi.org/10.3390/cli12120216 - 10 Dec 2024
Abstract
Climate change presents one of the biggest global threats to society, while the impact of its manifestations on human health has been poorly characterized and quantified, especially in middle- and low-income countries. The perceptual views of health professionals about the climate and health
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Climate change presents one of the biggest global threats to society, while the impact of its manifestations on human health has been poorly characterized and quantified, especially in middle- and low-income countries. The perceptual views of health professionals about the climate and health nexus are critical for the effective implementation of climate policies. The Sierra Leone health professionals are no exception to this, and no such data exist for their country. To this extent, we distributed a cross-sectional survey to understand the perceptual views and beliefs of health professionals in Sierra Leone about the climate and health nexus. A validated international questionnaire on the topic was electronically administered to 265 participants. A descriptive analysis of the survey responses was conducted. Results showed that almost all of the respondents (97%) felt that climate change is an important issue; more than half (68%) of them were very worried about climate change, and 28% were somewhat worried. About half of respondents believed that human activities mostly caused climate change, while 40% of health professionals felt this was equally caused by human activities and natural changes in the environment. The need to engage health professionals with the public and policymakers to bring the health effects of climate change to their attention was particularly highlighted; however, most respondents (81%) stated that numerous barriers impede them from doing so. The most widely reported barriers and needs were the need for training to communicate effectively about climate change and health (96%) and guidance on creating sustainable workplaces (94%), followed by the need for lifelong training and education programs on climate and health, and the lack of time (73%). These survey findings would be valuable to policymakers in Sierra Leone and the broader African regions towards mitigating and adapting to climate change threats to human health.
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(This article belongs to the Special Issue Confronting the Climate Change and Health Nexus: Interactions, Impacts, and Adaptation Strategies)
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Open AccessArticle
Evolution of Bioclimatic Belts in Spain and the Balearic Islands (1953–2022)
by
Christian Lorente, David Corell, María José Estrela, Juan Javier Miró and David Orgambides-García
Climate 2024, 12(12), 215; https://doi.org/10.3390/cli12120215 - 10 Dec 2024
Abstract
This study examines the spatio-temporal evolution of bioclimatic belts in peninsular Spain and the Balearic Islands from 1953 to 2022 using the World Bioclimatic Classification System and data from 3668 meteorological stations. Findings indicate a shift toward warmer and more arid conditions, with
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This study examines the spatio-temporal evolution of bioclimatic belts in peninsular Spain and the Balearic Islands from 1953 to 2022 using the World Bioclimatic Classification System and data from 3668 meteorological stations. Findings indicate a shift toward warmer and more arid conditions, with thermotypes showing an increase in mesomediterranean and thermomediterranean types and a decrease in mesotemperate and supratemperate types. Ombrotype analysis revealed a rise in semiarid types and a decline in humid and hyperhumid types. Significant changes occurred in climate transition zones and mountainous regions, where a process of “Mediterraneanisation”—a process characterised by the expansion of warmer and drier conditions typical of Mediterranean climates into previously temperate areas and/or an altitudinal rise in thermotypes—has been observed. The spatial variability of changes in ombrotypes was greater than that in thermotypes, with regions showing opposite trends to the general one. These results highlight the need for adaptive conservation strategies, particularly in mountainous and climate transition areas, where endemic species may face increased vulnerability due to habitat loss and fragmentation. The results of this study provide insight into how climate change is affecting bioclimatological conditions in the Iberian Peninsula and the Balearic Islands.
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(This article belongs to the Special Issue Climate Variability in the Mediterranean Region)
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Resilience of Chinese Ports to Tropical Cyclones: Operational Efficiency and Strategic Importance
by
Mark Ching-Pong Poo, Wen Zhang, Leila Kamalian, Tianni Wang, Yui-yip Lau and Tina Ziting Xu
Climate 2024, 12(12), 214; https://doi.org/10.3390/cli12120214 - 9 Dec 2024
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This study evaluated the resilience of five major Chinese ports—Shanghai, Tsingtao, Shenzhen, Xiamen, and Qinzhou—against the impacts of tropical cyclones. These ports, as integral global maritime supply chain nodes, face rising vulnerabilities from climate-related disruptions such as typhoons, sea-level rise, and extreme temperature
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This study evaluated the resilience of five major Chinese ports—Shanghai, Tsingtao, Shenzhen, Xiamen, and Qinzhou—against the impacts of tropical cyclones. These ports, as integral global maritime supply chain nodes, face rising vulnerabilities from climate-related disruptions such as typhoons, sea-level rise, and extreme temperature fluctuations. Employing a resilience assessment framework, this study integrated climate and operational data to gauge how cyclone-induced events affect port performance, infrastructure, and economic stability. Multi-centrality analysis and the Borda count method were applied to assess each port’s strategic importance and operational efficiency under cyclone exposure. The findings highlight variations in resilience across the ports, with Shanghai and Tsingtao showing heightened risk due to their critical roles within international logistics networks. This study suggests strategies like strengthening infrastructure, improving emergency responses, and adopting climate-resilient policies to make China’s ports more sustainable and resilient to climate threats. This research offers actionable insights for policymakers and port authorities, contributing to a more climate-resilient maritime logistics framework.
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The Role of Psychological Capital on Climate Change Adaptation Among Smallholder Farmers in the uMkhanyakude District of KwaZulu-Natal, South Africa
by
Mbongeni Maziya, Lelethu Mdoda and Lungile Pearl Sindiswa Mvelase
Climate 2024, 12(12), 213; https://doi.org/10.3390/cli12120213 - 8 Dec 2024
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Climate change and variability pose a challenge to the livelihoods of smallholder farmers. Previous studies on climate change in the context of smallholder farming have mainly focused on the influence of socio-economic factors in understanding farmers’ responses to climate change. However, little is
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Climate change and variability pose a challenge to the livelihoods of smallholder farmers. Previous studies on climate change in the context of smallholder farming have mainly focused on the influence of socio-economic factors in understanding farmers’ responses to climate change. However, little is known about the effect of psychological capital on climate change adaptation. There are calls for better empirical models and transdisciplinary approaches to understand the underlying drivers of climate change adaptation in smallholder farming systems. This study draws from behavioural decision research to assess psychological factors influencing climate change adaptation in the uMkhanyakude district of KwaZulu-Natal. This study adopted the Theory of Planned Behaviour to understand the effect of psychological capital on climate change adaptation. Data were collected from a sample of 400 smallholder farmers who were randomly selected from the uMkhanyakude district. Survey data were analysed using a multivariate probit regression model. The results of the multivariate probit regression model indicated that psychological capital (attitudes towards climate change, subjective norms, and trust) played an important role in influencing climate change adaptation. Climate change adaptation is also influenced by the gender of the farmer, education level, household size, and Tropical Livestock Units. These findings underscore the role of psychological capital in shaping climate change adaptation. This study recommends using transdisciplinary approaches (i.e., combining economics and psychology) in evaluating farmers’ responses to climate change.
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Decoding Carbon Footprints: How U.S. Climate Zones Shape Building Emissions
by
Ali Nouri and Ming Hu
Climate 2024, 12(12), 212; https://doi.org/10.3390/cli12120212 - 6 Dec 2024
Abstract
The construction industry accounts for over 40% of carbon emissions in the United States, with embodied carbon—emissions associated with building materials and construction processes—remaining underexplored, particularly regarding the impact of location and climate. This study addresses this gap by investigating the influence of
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The construction industry accounts for over 40% of carbon emissions in the United States, with embodied carbon—emissions associated with building materials and construction processes—remaining underexplored, particularly regarding the impact of location and climate. This study addresses this gap by investigating the influence of different climate zones on the embodied carbon emissions of residential buildings. Using Building Information Modeling (BIM), 3D models were developed based on the 2021 International Energy Conservation Code (IECC) and International Residential Code (IRC). A lifecycle assessment (LCA) was conducted using Environmental Product Declarations (EPDs) to evaluate the embodied carbon of building materials during the product stage. The findings reveal that buildings in colder climates exhibit higher embodied carbon emissions, ranging from 25,768 kgCO2e in Zone 1 to 40,129 kgCO2e in Zone 8, due to increased insulation requirements. Exterior walls and roofs were identified as significant contributors, comprising up to 34% of total emissions. Sensitivity analysis further indicates that the window-to-wall ratio and interior wall design substantially affect embodied carbon, with baseline emissions around 170 kgCO2e/m2 in warm areas and 255 kgCO2e/m2 in cold areas. These results establish a baseline for lifecycle embodied carbon values across different climate zones in the United States and align with international standards. This study provides valuable insights for policymakers and designers, offering data to inform effective carbon reduction strategies and optimize building designs for sustainability.
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(This article belongs to the Section Climate and Environment)
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