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Volume 13, January
 
 

Climate, Volume 13, Issue 2 (February 2025) – 22 articles

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18 pages, 1124 KiB  
Article
Climate Change Exposure of Agriculture Within Regulated Groundwater Basins of the Southwestern United States
by Lauren E. Parker, Ning Zhang, Isaya Kisekka, John T. Abatzoglou, Emile H. Elias, Caitriana M. Steele and Steven M. Ostoja
Climate 2025, 13(2), 42; https://doi.org/10.3390/cli13020042 (registering DOI) - 16 Feb 2025
Abstract
Agriculture is an important part of the economy of southwestern United States (Southwest). The production of food and fiber in the Southwest is supported by irrigation, much of which is sourced from groundwater. Climate projections suggest an increasing risk of drought and heat, [...] Read more.
Agriculture is an important part of the economy of southwestern United States (Southwest). The production of food and fiber in the Southwest is supported by irrigation, much of which is sourced from groundwater. Climate projections suggest an increasing risk of drought and heat, which can affect water supply and demand, and will challenge the future of agricultural production in the Southwest. Also, as groundwater in the Southwest is highly regulated, producers may not be able to readily rely on groundwater to meet increased demand. Climate exposure of five economically-important crops—alfalfa, cotton, pecans, pistachios, and processing tomatoes—was analyzed over twelve regulated groundwater basins by quantifying changes in a suite of both crop-specific and non-specific agroclimatic indicators between contemporary (1981–2020) and future (2045–2074, SSP2-4.5) climates. Generally, groundwater basins that are currently the most exposed to impactful climate conditions remain so under future climate. The crops with the greatest increase in exposure to their respective crop-specific indicators are cotton, which may be impacted by a ~180% increase in exposure to extreme heat days above 38 °C, and processing tomatoes, which may see a ~158% increase in exposure to high temperatures and reduced diurnal temperature range during flowering. These results improve understanding of the potential change in exposure to agroclimatic indicators, including crop-specific indicators, at the scale of regulated groundwater basins. This understanding provides useful information for the long-term implications of climate change on agriculture and agricultural water, and can inform adaptation efforts for coupled agricultural and water security in groundwater-dependent regions. These results may also be useful for assessing the adaptive potential of water conservation actions—some of which are outlined herein—or the suitability of other adaptation responses to the challenges that climate change will pose to agriculture. Full article
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23 pages, 2958 KiB  
Article
Expected Impacts on Mediterranean Forest Species Under Climate Change
by Álvaro Enríquez-de-Salamanca
Climate 2025, 13(2), 41; https://doi.org/10.3390/cli13020041 - 14 Feb 2025
Abstract
Climate change affects tree species, altering their growth and distribution, with effects varying by region, although mostly negative in the Mediterranean. This study examines 27 tree species in central Iberia, in a continental Mediterranean climate, using GISs and climate models. It investigates changes [...] Read more.
Climate change affects tree species, altering their growth and distribution, with effects varying by region, although mostly negative in the Mediterranean. This study examines 27 tree species in central Iberia, in a continental Mediterranean climate, using GISs and climate models. It investigates changes in net primary productivity (NPP) under different climate scenarios, identifying species that are endangered or vulnerable. Currently, only 2.4% of forest stands are endangered, but 51.2% are vulnerable; by 2100, these figures could rise to 35.4% and 85.2%, respectively. A correlation between altitude and threat level was found, with mountain species facing lower risks. Species with higher threat levels are linked to high NPP or low NPP variability. Four species currently have no threatened stands, though they may in the future, except one introduced in high-elevation areas, which will be favoured by climate change. Climate change will induce migrations to higher altitudes, but these movements depend on the rate of change, population size, fragmentation, and human alteration of the environment. Migration will be more challenging for low-altitude species in heavily human-impacted areas. Full article
(This article belongs to the Special Issue Forest Ecosystems under Climate Change)
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19 pages, 250 KiB  
Article
Perceptions of the Barriers to the Implementation of a Successful Climate Change Policy in Bulgaria
by Antonina Atanasova and Kliment Naydenov
Climate 2025, 13(2), 40; https://doi.org/10.3390/cli13020040 - 13 Feb 2025
Abstract
Climate change is increasingly recognized as a significant issue facing humanity. The World Health Organization (WHO) designates climate change as the greatest threat to global health in the 21st century. Bulgaria is under imminent threat from climate change. The country is projected to [...] Read more.
Climate change is increasingly recognized as a significant issue facing humanity. The World Health Organization (WHO) designates climate change as the greatest threat to global health in the 21st century. Bulgaria is under imminent threat from climate change. The country is projected to experience a temperature increase of up to 4 °C by 2100. This will lead to changes in precipitation patterns, resulting in numerous consequences. These include reduced water storage, impacts on public health, disruptions in agricultural production, stress on the country’s biodiversity and forests, damage to infrastructure and private property, changes in tourism patterns, and many other potential issues. Climate change has recently become a significant concern in Bulgaria due to its impact on ecosystems, the economy, society, and infrastructure. This study provides a comprehensive analysis of the barriers to climate adaptation in Bulgaria, integrating sources from the literature with empirical data gathered from a survey. By employing cluster analysis, this research identifies five primary groups of barriers, offering a fresh perspective on the complexities involved in this process. The findings contribute to the existing body of knowledge on climate adaptation and hold the potential to guide policy development aimed at addressing these challenges. Full article
16 pages, 2860 KiB  
Article
Analysis of the Dynamics of Hydroclimatic Extremes in Urban Areas: The Case of Grand-Nokoué in Benin, West Africa
by Vidjinnagni Vinasse Ametooyona Azagoun, Kossi Komi, Expédit Wilfrid Vissin and Komi Selom Klassou
Climate 2025, 13(2), 39; https://doi.org/10.3390/cli13020039 - 12 Feb 2025
Abstract
As global warming continues, extremes in key climate parameters will become more frequent. These extremes are one of the main challenges for the sustainability of cities. The aim of this study is to provide a better understanding of the evolution of extremes in [...] Read more.
As global warming continues, extremes in key climate parameters will become more frequent. These extremes are one of the main challenges for the sustainability of cities. The aim of this study is to provide a better understanding of the evolution of extremes in precipitation (pcp) and maximum (Tmax) and minimum (Tmin) temperatures in Grand-Nokoué to improve the resilience of the region. To this end, historical daily precipitation and maximum (Tmax) and minimum (Tmin) temperature data from the Cotonou synoptic station were used from 1991 to 2020. First, the extreme events identified using the 99th percentile threshold were used to analyze their annual and monthly frequency. Secondly, a Generalized Extreme Value (GEV) distribution was fitted to the annual maxima with a 95% confidence interval to determine the magnitude of the specific return periods. The parameters of this distribution were estimated using the method of L moments, considering non-stationarity. The results of the study showed significant upward trends in annual precipitation and minimum temperatures, with p-values of 0.04 and 0.001, respectively. Over the past decade, the number of extreme precipitation and Tmin events has exceeded the expected number. The model provides greater confidence for periods ≤ 50 years. Extreme values of three-day accumulations up to 68.21 mm for pcp, 79.38 °C for Tmin and 97.29 °C for Tmax are expected every two years. The results of this study can be used to monitor hydroclimatic hazards in the region. Full article
(This article belongs to the Section Climate and Environment)
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16 pages, 2687 KiB  
Article
The Relationship Between the Occurrence of Fires and Family Farming in Municipalities in the State of São Paulo, Brazil
by Leonardo Pinto de Magalhães, Anderson de Souza Gallo, Guilherme Honório Fernandez, Adriana Cavalieri Sais and Renata Evangelista de Oliveira
Climate 2025, 13(2), 38; https://doi.org/10.3390/cli13020038 - 11 Feb 2025
Abstract
In recent years, particularly in 2024, there has been an escalation in the frequency and intensity of megafires in the state of São Paulo, Brazil. This state, the most industrialized in the country, has seen extensive land-use changes in recent decades, with agriculture [...] Read more.
In recent years, particularly in 2024, there has been an escalation in the frequency and intensity of megafires in the state of São Paulo, Brazil. This state, the most industrialized in the country, has seen extensive land-use changes in recent decades, with agriculture extending upon areas previously dedicated to other uses and forests. The practice of family farming, which is distinguished by its smaller operational areas and the majority involvement of the family that owns the land, has the potential to influence the occurrence of fires, but few studies have explored the link between agricultural practices (especially the difference between family and other farming types) and fire intensity. This study aims to assess whether the higher presence of family-farming establishments in different municipalities reduces fire incidents. The results indicate that the municipalities with the highest presence of family farming present lower percentages of burned areas. The increased diversity in crop types and the presence of forest cover within these municipalities have been identified as contributing factors to this reduced fire rate and burned areas. These findings underscore the need for public policies that support family farming as a strategy to reduce fires and protect vulnerable farmers in rural landscapes. Full article
(This article belongs to the Special Issue Climate Adaptation Ways for Smallholder Farmers)
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19 pages, 3908 KiB  
Article
Scaling Properties of Rainfall as a Basis for Intensity–Duration–Frequency Relationships and Their Spatial Distribution in Catalunya, NE Spain
by María del Carmen Casas-Castillo, Alba Llabrés-Brustenga, Raül Rodríguez-Solà, Anna Rius and Àngel Redaño
Climate 2025, 13(2), 37; https://doi.org/10.3390/cli13020037 - 8 Feb 2025
Abstract
The spatial distribution of rainfall intensity–duration–frequency (IDF) values, essential for hydrological applications, were estimated for Catalunya, Spain. From a larger database managed by the Meteorological Service of Catalunya and after rigorous quality control, 163 high-quality daily series spanning from 1942 to 2016, with [...] Read more.
The spatial distribution of rainfall intensity–duration–frequency (IDF) values, essential for hydrological applications, were estimated for Catalunya, Spain. From a larger database managed by the Meteorological Service of Catalunya and after rigorous quality control, 163 high-quality daily series spanning from 1942 to 2016, with an average length of 39.8 years and approximately one station per 200 km2, were selected. A monofractal downscaling methodology was applied to derive rainfall intensities for sub-daily durations using the intensities from a reference 24 h duration as the basis, followed by spatial interpolations on a 1 km × 1 km grid. The scaling parameter values have been found to be higher in the northwestern mountainous areas, influenced by Atlantic climate, and lower in the central–western driest zones. A general negative gradient was observed toward the coastline, reflecting the increasing influence of the Mediterranean Sea. The IDF results are presented as spatial distribution maps, providing intensity–frequency estimates for durations between one hour and one day, and return periods between 2 and 200 years, with an estimated uncertainty below 12% for the 200-year return period, and lower for shorter return periods. These findings highlight the need to capture rainfall spatial variations for urban planning, flood control, and climate resilience efforts. Full article
(This article belongs to the Special Issue Advances of Flood Risk Assessment and Management)
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18 pages, 4831 KiB  
Article
Future Projections of Clouds and Precipitation Patterns in South Asia: Insights from CMIP6 Multi-Model Ensemble Under SSP5 Scenarios
by Praneta Khardekar, Rohini Lakshman Bhawar, Vinay Kumar and Hemantkumar S. Chaudhari
Climate 2025, 13(2), 36; https://doi.org/10.3390/cli13020036 - 8 Feb 2025
Abstract
Projecting future changes in monsoon rainfall is crucial for effective water resource management, food security, and livestock sustainability in South Asia. This study assesses precipitation, total cloud cover (categorized by cloud top pressure), and outgoing longwave radiation (OLR) across the region using Coupled [...] Read more.
Projecting future changes in monsoon rainfall is crucial for effective water resource management, food security, and livestock sustainability in South Asia. This study assesses precipitation, total cloud cover (categorized by cloud top pressure), and outgoing longwave radiation (OLR) across the region using Coupled Model Intercomparison Project Phase 6 (CMIP6) data. A multi-model ensemble (MME) approach is employed to analyze future projections under the Shared Socio-Economic Pathway (SSP5-8.5) scenario, which assumes radiative forcing will reach 8.5 W/m2 by 2100. The MME projects a ~1.5 mm/day increase in total rainfall during 2081–2100. Convective and stratiform precipitation are expected to expand spatially, with convective rainfall increasing from 3 mm/day in historical simulations to 3.302 mm/day in the far future. Stratiform precipitation also shows an increase from 0.822 mm/day to 0.962 mm/day over the same period. A notable decrease in OLR (~60 W/m2 along the Western Ghats) and an increase in high cloud cover suggest intensified monsoon rainfall. The pattern correlation coefficient (PCC) reveals reduced OLR in future scenarios (PCC ~0.77 vs. ~0.81 historically), likely due to cloud feedback mechanisms. These results highlight enhanced monsoonal activity under warming scenarios, with implications for regional climate adaptation. Full article
(This article belongs to the Special Issue Coping with Flooding and Drought)
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21 pages, 1868 KiB  
Review
Climate Change and Arbovirus: A Review and Bibliometric Analysis
by Maryly Weyll Sant’Anna, Maurício Lamano Ferreira, Leonardo Ferreira da Silva and Pedro Luiz Côrtes
Climate 2025, 13(2), 35; https://doi.org/10.3390/cli13020035 - 6 Feb 2025
Abstract
The rise in Earth’s temperature is capable of influencing the occurrence of catastrophic natural events, contributing to outbreaks of arboviruses in endemic areas and new geographical regions. This study aimed to conduct a bibliometric review and analysis of research activities on climate change [...] Read more.
The rise in Earth’s temperature is capable of influencing the occurrence of catastrophic natural events, contributing to outbreaks of arboviruses in endemic areas and new geographical regions. This study aimed to conduct a bibliometric review and analysis of research activities on climate change with a focus on human arboviruses, using the Scopus database. A total of 1644 documents were found related to the topic between 1934 and 2023. The United States continues to lead in the number of academic publications. Dengue was the arbovirosis with the highest number of publications, followed by West Nile fever, Zika and chikungunya fever. Due to the rise in global temperature, a trend of arbovirus dissemination to non-endemic areas is observed, with a possible global increase in morbidity and mortality. Consequently, more effective measures are expected from epidemiological surveillance, vector control services, governmental authorities and, crucially, social engagement in combating and preventing new outbreaks. Full article
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17 pages, 7075 KiB  
Article
Snow Cover and Depth Climatology and Trends in Greece
by Ioannis Masloumidis, Stavros Dafis, George Kyros, Konstantinos Lagouvardos and Vassiliki Kotroni
Climate 2025, 13(2), 34; https://doi.org/10.3390/cli13020034 - 6 Feb 2025
Abstract
The rising surface temperatures driven by climate change have resulted in significant reductions in snow depth and snow cover duration globally, with pronounced impacts on snow-dependent regions. This study focuses on Greece, a region where snow plays a critical role in water resources [...] Read more.
The rising surface temperatures driven by climate change have resulted in significant reductions in snow depth and snow cover duration globally, with pronounced impacts on snow-dependent regions. This study focuses on Greece, a region where snow plays a critical role in water resources and winter tourism. Using numerical model reanalysis data spanning from 1991 to 2020, this study identifies statistically significant declining trends in snow depth and duration of snow cover across much of the country. The findings reveal considerable spatial and temporal variability, with the most pronounced reductions occurring in winter months and mountainous regions. Particularly affected are the northern and central mountainous areas, where snow cover days have decreased by up to 1.5 days per year. Ski resorts at lower elevations exhibit steeper declines in snow reliability compared to higher-altitude resorts, posing challenges to winter tourism. These trends underscore the urgency of adaptation strategies for climate resilience in snow-dependent sectors and the broader implications for water resource management in the region. Full article
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18 pages, 2266 KiB  
Article
Soybean Yield Modeling and Analysis with Weather Dynamics in the Greater Mississippi River Basin
by Weiwei Xie, Yanbo Huang and Qingmin Meng
Climate 2025, 13(2), 33; https://doi.org/10.3390/cli13020033 - 6 Feb 2025
Abstract
Accurate crop yield prediction and modeling are essential for ensuring food security, optimizing resource allocation, and guiding policy decisions in agriculture, ultimately benefiting society at large. With the increasing threat of weather change, it is important to understand the impacts of weather dynamics [...] Read more.
Accurate crop yield prediction and modeling are essential for ensuring food security, optimizing resource allocation, and guiding policy decisions in agriculture, ultimately benefiting society at large. With the increasing threat of weather change, it is important to understand the impacts of weather dynamics on agricultural productivity, particularly for crucial crops like soybeans. This study considers the study area of the Greater Mississippi River Basin, where most soybeans are typically planted, with a large variety of weather across from the North to the South in the US. Leveraging the greenness and density measured by the normalized difference vegetation index (NDVI) from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images, along with weather variables including mean precipitation, minimum temperature, and maximum temperature, we aim to uncover the relationships between these variables and soybean yield for different geographical and weather regions. Our analysis focuses on the four weather regions within the US: Very Cold, Cold, Mixed Humid, and Hot Humid, where most soybeans are planted in the Mississippi River Basin. The findings reveal that soybean yield in the Cold and Very Cold regions is positively correlated with minimum temperatures, whereas in the Mixed Humid and Hot Humid regions, negative correlations between maximum temperatures and yields are found. We identify a significant positive correlation between precipitation and soybean yield across all regions. In addition, the NDVI shows significant positive correlations with the soybean yield. Both linear and nonlinear regression models, including support vector machine and random forest models, are trained with remotely sensed data and weather data, showing a reliable and improved crop yield prediction. The findings of this study contribute to a better understanding of how soybean yield responds to climatic variations and will help the national agricultural management system in better monitoring and predicting crop yield when facing the increasing challenge of weather dynamics. Full article
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19 pages, 4919 KiB  
Review
A Systematic Review of Effective Measures to Resist Manipulative Information About Climate Change on Social Media
by Aliaksandr Herasimenka, Xianlingchen Wang and Ralph Schroeder
Climate 2025, 13(2), 32; https://doi.org/10.3390/cli13020032 - 5 Feb 2025
Abstract
We present a systematic review of peer-reviewed research into ways to mitigate the spread of manipulative information about climate change on social media (n = 38). Such information may include disinformation, harmful influence campaigns, or the unintentional spread of misleading information. We [...] Read more.
We present a systematic review of peer-reviewed research into ways to mitigate the spread of manipulative information about climate change on social media (n = 38). Such information may include disinformation, harmful influence campaigns, or the unintentional spread of misleading information. We find that the commonly recommended approaches to addressing manipulation of climate change belief include corrective information sharing and education campaigns targeting media literacy. However, most of the relevant research fails to test the approaches and interventions it proposes. We locate research gaps that include a lack of attention to the large commercial and political entities involved in generating and disseminating manipulation; video- and image-focused platforms; and the computational methods used to collect and analyze data. Evidence drawn from many studies demonstrates an emerging consensus about the policies required to resist climate change manipulation. Full article
(This article belongs to the Section Policy, Governance, and Social Equity)
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25 pages, 8059 KiB  
Article
Next-Generation Drought Intensity–Duration–Frequency Curves for Early Warning Systems in Ethiopia’s Pastoral Region
by Getachew Tegegne, Sintayehu Alemayehu, Sintayehu W. Dejene, Liyuneh Gebre, Tadesse Terefe Zeleke, Lidya Tesfaye and Numery Abdulhamid
Climate 2025, 13(2), 31; https://doi.org/10.3390/cli13020031 - 2 Feb 2025
Abstract
The pastoral areas of Ethiopia are facing a recurrent drought crisis that significantly affects the availability of water resources for communities dependent on livestock. Despite the urgent need for effective drought early warning systems, Ethiopia’s pastoral areas have limited capacities to monitor variations [...] Read more.
The pastoral areas of Ethiopia are facing a recurrent drought crisis that significantly affects the availability of water resources for communities dependent on livestock. Despite the urgent need for effective drought early warning systems, Ethiopia’s pastoral areas have limited capacities to monitor variations in the intensity–duration–frequency of droughts. This study intends to drive drought intensity–duration–frequency (IDF) curves that account for climate-model uncertainty and spatial variability, with the goal of enhancing water resources management in Borana, Ethiopia. To achieve this, the study employed quantile delta mapping to bias-correct outputs from five climate models. A novel multi-model ensemble approach, known as spatiotemporal reliability ensemble averaging, was utilized to combine climate-model outputs, exploiting the strengths of each model while discounting their weaknesses. The Standardized Precipitation Evaporation Index (SPEI) was used to quantify meteorological (3-month SPEI), agricultural (6-month SPEI), and hydrological (12-month SPEI) droughts. Overall, the analysis of historical (1990–2014) and projected (2025–2049, 2050–2074, and 2075–2099) periods revealed that climate change significantly exacerbates drought conditions across all three systems, with changes in drought being more pronounced than changes in mean precipitation. A prevailing rise in droughts’ IDF features is linked to an anticipated decline in precipitation and an increase in temperature. From the derived drought IDF curves, projections for 2025–2049 and 2050–2074 indicate a significant rise in hydrological drought occurrences, while the historical and 2075–2099 periods demonstrate greater vulnerability in meteorological and agricultural systems. While the frequency of hydrological droughts is projected to decrease between 2075 and 2099 as their duration increases, the periods from 2025 to 2049 and from 2050 to 2074 are expected to experience more intense hydrological droughts. Generally, the findings underscore the critical need for timely interventions to mitigate the vulnerabilities associated with drought, particularly in areas like Borana that depend heavily on water resources availability. Full article
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22 pages, 9741 KiB  
Article
Assessing Green Strategies for Urban Cooling in the Development of Nusantara Capital City, Indonesia
by Radyan Putra Pradana, Vinayak Bhanage, Faiz Rohman Fajary, Wahidullah Hussainzada, Mochamad Riam Badriana, Han Soo Lee, Tetsu Kubota, Hideyo Nimiya and I Dewa Gede Arya Putra
Climate 2025, 13(2), 30; https://doi.org/10.3390/cli13020030 - 31 Jan 2025
Abstract
The relocation of Indonesia’s capital to Nusantara in East Kalimantan has raised concerns about microclimatic impacts resulting from proposed land use and land cover (LULC) changes. This study explored strategies to mitigate these impacts by using dynamical downscaling with the Weather Research and [...] Read more.
The relocation of Indonesia’s capital to Nusantara in East Kalimantan has raised concerns about microclimatic impacts resulting from proposed land use and land cover (LULC) changes. This study explored strategies to mitigate these impacts by using dynamical downscaling with the Weather Research and Forecasting model integrated with the urban canopy model (WRF-UCM). Numerical experiments at a 1 km spatial resolution were used to evaluate the impacts of green and mitigation strategies on the proposed master plan. In this process, five scenarios were analyzed, incorporating varying proportions of blue–green spaces and modifications to building walls and roof albedos. Among them, scenario 5, with 65% blue–green spaces, exhibited the highest cooling potential, reducing average urban surface temperatures by approximately 2 °C. In contrast, scenario 4, which allocated equal shares of built-up areas and mixed forests (50% each), achieved a more modest reduction of approximately 1 °C. The adoption of nature-based solutions and sustainable urban planning in Nusantara underscores the feasibility of climate-resilient urban development. This framework could inspire other cities worldwide, showcasing how urban growth can align with environmental sustainability. Full article
(This article belongs to the Special Issue Applications of Smart Technologies in Climate Risk and Adaptation)
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22 pages, 999 KiB  
Article
Preparedness, Response, and Communication Preferences of Dairy Farmers During Extreme Weather Events: A Phenomenological Case Study
by Emmanuel C. Okolo, Rafael Landaverde, David Doerfert, Juan Manuel Piñeiro, Darren Hudson, Chanda Elbert and Kelsi Opat
Climate 2025, 13(2), 29; https://doi.org/10.3390/cli13020029 - 31 Jan 2025
Abstract
In 2021, Winter Storm Uri severely affected several Texan agricultural sectors, including dairy production. To understand how dairy producers experienced this extreme weather event, this qualitative phenomenological case study explored perceptions of preparedness, coping strategies, and information needs and preferences for dealing with [...] Read more.
In 2021, Winter Storm Uri severely affected several Texan agricultural sectors, including dairy production. To understand how dairy producers experienced this extreme weather event, this qualitative phenomenological case study explored perceptions of preparedness, coping strategies, and information needs and preferences for dealing with extreme weather events among dairy producers in Texas, conducting individual semi-structured interviews. The findings indicated that farmers felt unprepared to deal with extreme weather events and suffered significant economic losses due to this lack of preparedness. In response to winter storm Uri, dairy farmers modified traditional operations and management practices to mitigate negative impacts on farm labor, infrastructure, and herds. Our results, along with the existing literature on communication for extreme weather event management, highlighted that dairy farmers do not receive adequate information to effectively prevent and cope with similar occurrences in the future. Consequently, this study recommends exploring effective strategies to help agricultural producers develop plans to manage the effects of extreme weather events. Additionally, it integrates place-based, pluralistic, and demand-driven approaches to identify the best communication practices, enhance timely information dissemination on extreme weather, and strengthen the technical capacities of public and private entities, including Cooperative Extension Systems, as trusted resources for agricultural producers. Full article
(This article belongs to the Section Climate Adaptation and Mitigation)
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20 pages, 3775 KiB  
Article
Snow Resources and Climatic Variability in Jammu and Kashmir, India
by Aaqib Ashraf Bhat, Poul Durga Dhondiram, Saurabh Kumar Gupta, Shruti Kanga, Suraj Kumar Singh, Gowhar Meraj, Pankaj Kumar and Bhartendu Sajan
Climate 2025, 13(2), 28; https://doi.org/10.3390/cli13020028 - 30 Jan 2025
Abstract
Climate change is profoundly impacting snow-dependent regions, altering hydrological cycles and threatening water security. This study examines the relationships between snow water equivalent (SWE), snow cover, temperature, and wind speed in Jammu and Kashmir, India, over five decades (1974–2024). Using ERA5 reanalysis and [...] Read more.
Climate change is profoundly impacting snow-dependent regions, altering hydrological cycles and threatening water security. This study examines the relationships between snow water equivalent (SWE), snow cover, temperature, and wind speed in Jammu and Kashmir, India, over five decades (1974–2024). Using ERA5 reanalysis and Indian Meteorological Department (IMD) datasets, we reveal significant declines in SWE and snow cover, particularly in high-altitude regions such as Kupwara and Bandipora. A Sen’s slope of 0.0016 °C per year for temperature highlights a steady warming trend that accelerates snowmelt, shortens snow cover duration, and reduces streamflow during critical agricultural periods. Strong negative correlations between SWE and temperature (r = −0.7 to −0.9) emphasize the dominant role of rising temperatures in SWE decline. Wind speed trends exhibit weaker correlations with SWE (r = −0.2 to −0.4), although localized effects on snow redistribution and evaporation are evident. Temporal snow cover analyses reveal declining winter peaks and diminished summer runoff contributions, exacerbating water scarcity. These findings highlight the cascading impacts of climate variability on snow hydrology, water availability, and regional ecosystems. Adaptive strategies, including real-time snow monitoring, sustainable water management, and climate-resilient agricultural practices, are imperative for mitigating these challenges in this sensitive Himalayan region. Full article
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14 pages, 249 KiB  
Article
Climate Change Worry in German University Students: Determinants and Associations with Health-Related Outcomes
by Andrea Söder, Raphael M. Herr, Tatiana Görig and Katharina Diehl
Climate 2025, 13(2), 27; https://doi.org/10.3390/cli13020027 - 29 Jan 2025
Abstract
Climate change is known to have an impact on human health, including mental health. To better understand this phenomenon, the Climate Change Worry Scale (CCWS), a 10-item questionnaire, was developed to assess climate change worry as a psychological response to climate change. The [...] Read more.
Climate change is known to have an impact on human health, including mental health. To better understand this phenomenon, the Climate Change Worry Scale (CCWS), a 10-item questionnaire, was developed to assess climate change worry as a psychological response to climate change. The aim of this study was to validate a German version of the CCWS among university students and to explore potential associations with health outcomes. The CCWS was translated into German and used in an online survey of 1105 university students. We tested the scale’s psychometric properties and assessed its associations with sociodemographic characteristics and health outcomes. These included the Somatic Symptom Scale-8, Jenkins Sleep Scale, WHO-5 Well-being Index, and Patient Health Questionnaire 8. All CCWS items loaded on one factor and the items showed high internal consistency. Positive associations were observed between climate change worry and self-reported somatic symptoms, sleep difficulties, mental well-being, and depressive symptoms in multivariate regression models. The German version of the CCWS is a valid tool to measure climate change worry and can be used in future studies. The association between the CCWS and mental health underscores the need to recognize that students perceive climate change as a serious threat. Full article
18 pages, 5715 KiB  
Article
Tree Crown Damage and Physiological Responses Under Extreme Heatwave in Heterogeneous Urban Habitat of Central China
by Li Zhang, Wenli Zhu, Ming Zhang and Xiaoyi Xing
Climate 2025, 13(2), 26; https://doi.org/10.3390/cli13020026 - 28 Jan 2025
Abstract
(1) Background: Global warming has intensified dry heatwaves, threatening urban tree health and ecosystem services. Crown damage in trees is a key indicator of heat stress, linked to physiological changes and urban habitat characteristics, but the specific mechanisms remain to be explored. (2) [...] Read more.
(1) Background: Global warming has intensified dry heatwaves, threatening urban tree health and ecosystem services. Crown damage in trees is a key indicator of heat stress, linked to physiological changes and urban habitat characteristics, but the specific mechanisms remain to be explored. (2) Methods: This study investigated the heatwave-induced crown damage of Wuhan’s urban tree species, focusing on the influence of physiological responses and urban habitats. Crown damage was visually scored, and physiological responses were measured via stomatal conductance (Gs) and transpiration rate (Tr). (3) Results: Significant interspecific differences in crown damage were identified, with Prunus × yedoensis showing the highest degree of crown damage, while Pittosporum tobira displayed the lowest. A strong correlation was observed between crown damage and Gs and Tr, albeit with species-specific variations. The Degree of Building Enclosure (DegBE) emerged as the most prominent habitat factor, with a mitigating effect on crown damage, followed by the Percentage of Canopy Coverage (PerCC), in contrast with the Percentage of Impermeable Surface (PerIS) that showed a significant positive correlation. (4) Conclusions: The above findings suggest that species traits and habitat configurations interact in complex ways to shape tree resilience under heatwave stress, informing strategies for urban vegetation protection against heat stress in Central Chinese cities. Full article
(This article belongs to the Topic Responses of Trees and Forests to Climate Change)
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29 pages, 31037 KiB  
Article
El Niño–Southern Oscillation Prediction Based on the Global Atmospheric Oscillation in CMIP6 Models
by Ilya V. Serykh
Climate 2025, 13(2), 25; https://doi.org/10.3390/cli13020025 - 27 Jan 2025
Abstract
In this work, the preindustrial control (piControl) and Historical experiments results from climatic Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are analyzed for their ability to predict the El Niño–Southern Oscillation (ENSO). Using the principal [...] Read more.
In this work, the preindustrial control (piControl) and Historical experiments results from climatic Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are analyzed for their ability to predict the El Niño–Southern Oscillation (ENSO). Using the principal component method, it is shown that the Global Atmospheric Oscillation (GAO), of which the ENSO is an element, is the main mode of interannual variability of planetary anomalies of surface air temperature (SAT) and atmospheric sea level pressure (SLP) in the ensemble of 50 CMIP6 models. It turns out that the CMIP6 ensemble of models reproduces the planetary structure of the GAO and its west–east dynamics with a period of approximately 3.7 years. The models showed that the GAO combines ENSO teleconnections with the tropics of the Indian and Atlantic Oceans, and with temperate and high latitudes. To predict strong El Niño and La Niña events, we used a predictor index (PGAO) obtained earlier from observation data and reanalyses. The predictive ability of the PGAO is based on the west–east propagation of planetary structures of SAT and SLP anomalies characteristic of the GAO. Those CMIP6 models have been found that reproduce well the west–east spread of the GAO, with El Niño and La Niña being phases of this process. Thanks to this, these events can be predicted with approximately a year’s lead time, thereby overcoming the so-called spring predictability barrier (SPB) of the ENSO. Thus, the influence of global anomalies of SAT and SLP on the ENSO is shown, taking into account that it may increase the reliability of the early forecast of El Niño and La Niña events. Full article
(This article belongs to the Section Climate Dynamics and Modelling)
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23 pages, 7975 KiB  
Article
Sub-Daily Performance of a Convection-Permitting Model in Simulating Decade-Long Precipitation over Northwestern Türkiye
by Cemre Yürük Sonuç, Veli Yavuz and Yurdanur Ünal
Climate 2025, 13(2), 24; https://doi.org/10.3390/cli13020024 - 24 Jan 2025
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Abstract
One of the main differences between regional climate model and convection-permitting model simulations is not just how well topographic characteristics are represented, but also how deep convection is treated. The convection process frequently occurs within hours, thus a sub-daily scale becomes appropriate to [...] Read more.
One of the main differences between regional climate model and convection-permitting model simulations is not just how well topographic characteristics are represented, but also how deep convection is treated. The convection process frequently occurs within hours, thus a sub-daily scale becomes appropriate to evaluate these changes. To do this, a series of simulations has been carried out at different spatial resolutions (0.11° and 0.025°) using the COSMO-CLM (CCLM) climate model forced by the ECMWF Reanalysis v5 (ERA5) between 2011 and 2020 over a domain covering northwestern Türkiye. Hourly precipitation and heavy precipitation simulated by both models were compared with the observations by Turkish State Meteorological Service (TSMS) stations and Integrated Multi-satellitE Retrievals for GPM (IMERG). Subsequently, we aimed to identify the reasons behind these differences by computing several atmospheric stability parameters and conducting event-scale analysis using atmospheric sounding data. CCLM12 displays notable discrepancies in the timing of the diurnal cycle, exhibiting a premature shift of several hours when compared to the TSMS. CCLM2.5 offers an accurate representation of the peak times, considering all hours and especially those occurring during the wet hours of the warm season. Despite this, there is a tendency for peak intensities to be overestimated. In both seasons, intensity and extreme precipitation are highly underestimated by CCLM12 compared to IMERG. In terms of statistical metrics, the CCLM2.5 model performs better than the CCLM12 model under extreme precipitation conditions. The comparison between CCLM12 and CCLM2.5 at 12:00 UTC reveals differences in atmospheric conditions, with CCLM12 being wetter and colder in the lower troposphere but warmer at higher altitudes, overestimating low-level clouds and producing lower TTI and KI values. These conditions can promote faster air saturation in CCLM12, resulting in lower LCL and CCL, which foster the development of low-level clouds and frequent low-intensity precipitation. In contrast, the simulation of higher TTI and KI values and a steeper lapse rate in CCLM2.5 enables air parcels to enhance instability, reach the LFC more rapidly, increase EL, and finally promote deeper convection, as evidenced by higher CAPE values and intense low-frequency precipitation. Full article
(This article belongs to the Section Climate Dynamics and Modelling)
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21 pages, 2371 KiB  
Article
Predicting Asthma Hospitalizations from Climate and Air Pollution Data: A Machine Learning-Based Approach
by Jean Souza dos Reis, Rafaela Lisboa Costa, Fabricio Daniel dos Santos Silva, Ediclê Duarte Fernandes de Souza, Taisa Rodrigues Cortes, Rachel Helena Coelho, Sofia Rafaela Maito Velasco, Danielson Jorge Delgado Neves, José Firmino Sousa Filho, Cairo Eduardo Carvalho Barreto, Jório Bezerra Cabral Júnior, Herald Souza dos Reis, Keila Rêgo Mendes, Mayara Christine Correia Lins, Thomás Rocha Ferreira, Mário Henrique Guilherme dos Santos Vanderlei, Marcelo Felix Alonso, Glauber Lopes Mariano, Heliofábio Barros Gomes and Helber Barros Gomes
Climate 2025, 13(2), 23; https://doi.org/10.3390/cli13020023 - 24 Jan 2025
Viewed by 267
Abstract
This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological [...] Read more.
This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological variables and pollutants, called exp1, and the second only meteorological variables, called exp2. For both experiments, tests were also carried out incorporating lagged information from the time series of asthma records. The models were trained on 80% of the data and validated on the remaining 20%. Among the five methods evaluated—random forest (RF), eXtreme Gradient Boosting (XGBoost), Multiple Linear Regression (MLR), support vector machine (SVM), and K-nearest neighbors (KNN)—the RF models showed superior performance, notably those of exp1 when incorporating lagged asthma notifications as an additional predictor. Minimum temperature and sulfur dioxide emerged as key variables, probably due to their associations with respiratory health and pollution levels, emphasizing their role in asthma exacerbation. The autocorrelation of the residuals was assessed due to the inclusion of lagged variables in some experiments. The results highlight the importance of pollutant and meteorological factors in predicting asthma cases, with implications for public health monitoring. Despite the limitations presented and discussed, this study demonstrates that forecast accuracy improves when a wider range of lagged variables are used, and indicates the suitability of RF for health datasets with complex time series. Full article
(This article belongs to the Special Issue New Perspectives in Air Pollution, Climate, and Public Health)
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19 pages, 2967 KiB  
Article
The Influence of Climate Variables on Malaria Incidence in Vanuatu
by Jade Sorenson, Andrew B. Watkins and Yuriy Kuleshov
Climate 2025, 13(2), 22; https://doi.org/10.3390/cli13020022 - 22 Jan 2025
Viewed by 356
Abstract
Malaria, a climate-sensitive mosquito-borne disease, is widespread in tropical and subtropical regions, and its elimination is a global health priority. Malaria is endemic to Vanuatu, where elimination campaigns have been implemented with varied success. In this study, climate variables were assessed for their [...] Read more.
Malaria, a climate-sensitive mosquito-borne disease, is widespread in tropical and subtropical regions, and its elimination is a global health priority. Malaria is endemic to Vanuatu, where elimination campaigns have been implemented with varied success. In this study, climate variables were assessed for their correlation with national malaria cases from 2014 to 2023 and used to develop a proof-of-concept model for estimating malaria incidence in Vanuatu. Maximum, minimum, and median temperatures; diurnal temperature variation; median temperature during the 18:00–21:00 mosquito biting period (VUT); median humidity; and precipitation (total and anomaly) were evaluated as predictors at different time lags. It was found that maximum temperature had the strongest correlation with malaria cases and produced the best-performing linear regression model, where malaria cases increased by approximately 43 cases for every degree (°C) increase in monthly maximum temperature. This aligns with similar findings from climate–malaria studies in the Southwest Pacific, where temperature tends to stimulate the development of both Anopheles farauti and Plasmodium vivax, increasing transmission probability. A Bayesian model using maximum temperature and total precipitation at a two-month time lag was more effective in predicting malaria incidence than using maximum temperature or precipitation alone. A Bayesian approach was preferred due to its flexibility with varied data types and prior information about malaria dynamics. This model for predicting malaria incidence in Vanuatu can be adapted to smaller regions or other malaria-affected areas, supporting malaria early warning and preparedness for climate-related health challenges. Full article
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25 pages, 8013 KiB  
Article
Daily Concentration of Precipitation in the Province of Alicante (1981–2020)
by Esther Sánchez-Almodóvar, Jorge Olcina-Cantos, Javier Martin-Vide and Javier Martí-Talavera
Climate 2025, 13(2), 21; https://doi.org/10.3390/cli13020021 - 22 Jan 2025
Viewed by 326
Abstract
The precipitation in the Mediterranean region, characterised by its annual variability and concentration in high-intensity events, is a key factor in territorial planning and the management of runoff in urban areas, particularly on the Spanish Mediterranean coast. This study focuses on the province [...] Read more.
The precipitation in the Mediterranean region, characterised by its annual variability and concentration in high-intensity events, is a key factor in territorial planning and the management of runoff in urban areas, particularly on the Spanish Mediterranean coast. This study focuses on the province of Alicante, applying the “daily precipitation concentration index (CI)” in 26 meteorological stations for the period 1981–2020, with the aim of analysing the statistical structure of precipitation on an annual scale. It measures the irregularity and intensity of precipitation according to the concentration of most of the annual total in a few days. Furthermore, it examines the synoptic situations and trajectories of the air masses on days of torrential rain using the HYSPLIT model. This is essential to identify the origin of moist air masses, to understand the meteorological mechanisms that intensify extreme rainfall events, and to identify recurrent patterns that explain their frequency and characteristics. The results reveal extreme CI values of between 0.58 in the interior of the province and 0.71 in the southern pre-coastal area, with a value of 0.68 in the city of Alicante. On average, the CI is 0.65, indicating that 25% of days with more rain have a concentration of around 75% of total precipitation, while 10% of the days represent 45% of the total. With respect to the origin of air masses, the most relevant in the mid-troposphere (500 hPa) are those from the north of Africa, particularly during the final periods of their trajectory, with flows from the east on the surface. Full article
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