Climate

22 pages, 4925 KiB

Open AccessArticle

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 (registering DOI) - 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)

22 pages, 999 KiB

Open AccessArticle

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, 5739 KiB

Open AccessArticle

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

12 pages, 347 KiB

Open AccessArticle

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

Open AccessArticle

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

Open AccessArticle

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, 4274 KiB

Open AccessArticle

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

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)

21 pages, 2371 KiB

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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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16 pages, 235 KiB

Open AccessEssay

Navigating Global Environmental Challenges: Disciplinarity, Transdisciplinarity, and the Emergence of Mega-Expertise

by Rolf Lidskog

Climate 2025, 13(1), 20; https://doi.org/10.3390/cli13010020 - 16 Jan 2025

Abstract

This study explores the nature and significance of a crucial form of global environmental expertise: that which relates to conducting global environmental assessments with the aim of influencing decision-making. Drawing on the theory of expertise, which conceptualizes expertise as a social position defined [...] Read more.

This study explores the nature and significance of a crucial form of global environmental expertise: that which relates to conducting global environmental assessments with the aim of influencing decision-making. Drawing on the theory of expertise, which conceptualizes expertise as a social position defined by epistemic practice, this study focuses on expertise in the context of global environmental challenges—particularly relating to climate change and the IPCC—highlighting the expertise required to address this kind of complex and multifaceted issue. This type of expertise allows for a synthesis of the current state of environmental challenges, the proposal of options for action, and communication of these findings to decision-makers and society at large. This expertise shapes knowledge that is much broader than a single disciplinary field, encompassing both ecological and social dynamics, and allows for the development of recommendations for action. This study finds that such expertise embodies a distinct epistemic practice with four key characteristics that distinguish it from more narrowly defined forms of expertise and introduces the term “mega-expertise” to capture the character and position of this kind of expertise. This study concludes by reflecting on the broader implications of this form of expertise, considering its relationship to more traditional, disciplinary scientific expertise. Full article

(This article belongs to the Section Policy, Governance, and Social Equity)

18 pages, 1801 KiB

Open AccessArticle

Projecting Climate Change Impacts on Benin’s Cereal Production by 2050: A SARIMA and PLS-SEM Analysis of FAO Data

by Kossivi Fabrice Dossa, Jean-François Bissonnette, Nathalie Barrette, Idiatou Bah and Yann Emmanuel Miassi

Climate 2025, 13(1), 19; https://doi.org/10.3390/cli13010019 - 16 Jan 2025

Abstract

Globally, agriculture is facing significant challenges due to climate change, which is seriously affecting grain yields. This research aims to analyze the significant effect of climate change (temperature and rainfall) on cereal production in Benin. The choice of Benin is explained by its [...] Read more.

Globally, agriculture is facing significant challenges due to climate change, which is seriously affecting grain yields. This research aims to analyze the significant effect of climate change (temperature and rainfall) on cereal production in Benin. The choice of Benin is explained by its strong dependence on agriculture and its vulnerability to climatic variations. This study employed climate and agricultural data from FAO and ASECNA (1990–2020) to evaluate the impacts of climate change on cereal production. SARIMA time-series models were used for forecasting, while the PLS-SEM approach assessed the relationships between climate variables and cereal production. The findings reveal a rise in temperatures and a gradual decline in precipitation. Despite these challenges, the time-series analysis suggests that Beninese farmers are expanding cultivated areas, successfully increasing production levels, and improving yields. Projections to 2050 indicate an increase in areas and production for maize and rice, while sorghum shows a constant trend. However, even with these projections, it is recommended to explore, in more depth, the resilience strategies used by cereal producers to better understand their influence and refine the orientations of future agricultural policies. Full article

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22 pages, 6774 KiB

Open AccessArticle

Effect of Short Duration Heat Stress on the Physiological and Production Parameters of Holstein-Friesian Crossbred Dairy Cows in Bangladesh

by Mst. Umme Habiba, S. A. Masudul Hoque, Moin Uddin, Khatun-A-Jannat Esha, Sabrina Zaman Seema, Kazi Md. Al-Noman, Shamsun Nahar Tamanna, Shahrina Akhtar, Md. Abdus Salam, Abu Sadeque Md. Selim and Md. Morshedur Rahman

Climate 2025, 13(1), 18; https://doi.org/10.3390/cli13010018 - 13 Jan 2025

Abstract

Heat stress is a major concern for lactating dairy cows. This study evaluated the effects of heat stress on six Holstein-Friesian crossbred dairy cows exposed to three thermal conditions represented by the Temperature-Humidity Index (THI). These conditions included a baseline pre-treatment phase at [...] Read more.

Heat stress is a major concern for lactating dairy cows. This study evaluated the effects of heat stress on six Holstein-Friesian crossbred dairy cows exposed to three thermal conditions represented by the Temperature-Humidity Index (THI). These conditions included a baseline pre-treatment phase at THI-72, a heat stress treatment phase at THI-75 and THI-80, and a post-treatment recovery phase at THI-72. The duration of the heat stress treatment phase was 24 h. A total of four trials, each involving three cows, were conducted in an IoT-based climatic chamber to assess various physiological, hematological, biochemical, and production parameters across these phases. Compared to the baseline (THI-72), cows showed significant increases (p < 0.05) in rectal temperature (RT), heart rate (HR), respiration rate (RR), and water intake (WI) at both THI-75 and THI-80, with the highest elevations observed at THI-80 (RT: 5.1%, HR: 8.6%, RR: 23.5%, and WI: 19.1%). Feed intake declined significantly (p < 0.05) by 6.5% and 14.0%, and milk yield dropped by 5.3% and 14.7% at THI-75 and THI-80, respectively; milk fat and protein percentages decreased by 1.1-fold and 1.2-fold. Hemoglobin, platelet, and lymphocyte counts, along with biochemical parameters (excluding serum creatinine) also decreased significantly (p < 0.05). The different levels of THI influenced pairwise correlation patterns, with THI-75 showing intense interactions and THI-80 exhibiting greater variability. The findings highlight that Holstein-Friesian crossbred dairy cows are particularly vulnerable to heat stress, even with short-term exposure. This vulnerability can lead to economic losses for Bangladeshi dairy farmers rearing Holstein-Friesian crossbred cows. Full article

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32 pages, 11641 KiB

Open AccessArticle

The Performance of a High-Resolution WRF Modelling System in the Simulation of Severe Tropical Cyclones over the Bay of Bengal Using the IMDAA Regional Reanalysis Dataset

by Thatiparthi Koteshwaramma, Kuvar Satya Singh and Sridhara Nayak

Climate 2025, 13(1), 17; https://doi.org/10.3390/cli13010017 - 13 Jan 2025

Abstract

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the [...] Read more.

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement. Full article

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16 pages, 2665 KiB

Open AccessArticle

Using Hybrid Deep Learning Models to Predict Dust Storm Pathways with Enhanced Accuracy

by Mahdis Yarmohamadi, Ali Asghar Alesheikh and Mohammad Sharif

Climate 2025, 13(1), 16; https://doi.org/10.3390/cli13010016 - 12 Jan 2025

Abstract

As a potential consequence of climate change, the intensity and frequency of dust storms are increasing. A dust storm arises when strong winds blow loose dust from a dry surface, transporting soil particles from one place to another. The environmental and human health [...] Read more.

As a potential consequence of climate change, the intensity and frequency of dust storms are increasing. A dust storm arises when strong winds blow loose dust from a dry surface, transporting soil particles from one place to another. The environmental and human health impacts of dust storms are substantial. Accordingly, studying the monitoring of this phenomenon and predicting its pathways for early decision making and warning are vital. This study employs deep learning methods to predict dust storm pathways. Specifically, hybrid CNN-LSTM and ConvLSTM models have been proposed for the 24 h-ahead prediction of dust storms in the region under study. The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product that includes the dust particles and the meteorological information, such as surface wind speed and direction, relative humidity, surface air temperature, and skin temperature, is used to train the proposed models. These contextual features are selected utilizing the random forest feature importance method. The results indicate an improvement in the performance of both models by considering the contextual information. Moreover, a 0.2 increase in the Kappa coefficient criterion across all forecast hours indicates the CNN-LSTM model outperforms the ConvLSTM model when contextual information is considered. Full article

(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)

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16 pages, 12343 KiB

Open AccessArticle

Agroclimatic Zoning of Temperature Limitations for Growth of Stubble Cover Crops

by Jan Haberle, Filip Chuchma, Ivana Raimanova and Jana Wollnerova

Climate 2025, 13(1), 15; https://doi.org/10.3390/cli13010015 - 9 Jan 2025

Abstract

The realization of the expected benefits of stubble cover crops (CCs) depends on sufficient plant growth, which is influenced by the sum of effective temperatures (SET) before the onset of winter and the occurrence of the first early autumn frost (FRST). The objective [...] Read more.

The realization of the expected benefits of stubble cover crops (CCs) depends on sufficient plant growth, which is influenced by the sum of effective temperatures (SET) before the onset of winter and the occurrence of the first early autumn frost (FRST). The objective of this study was to calculate the SET for three dates of CC sowing, August 20 (A), September 6 (B), and September 20 (C), from 1961 to 2020, based on daily data from 268 meteorological stations in the Czech Republic (CR). The dates of FRST, when the daily average and minimum temperatures at 2 m and the minimum temperature at the ground level fell below 0 °C, −3, and −5 °C during CC growth, were recorded. The analysis showed a significant trend in the average SET, which increased by 1.60, 0.87, and 0.97 °C per year for scenarios A, B, and C, respectively. As a result, the area where SET conditions allowed for CC flowering from autumn sowing expanded, as visualized in the agroclimatic maps of the country. The average dates of the FRST shifted by 0.05–0.11 days per year over the sixty years, but this was not significant due to high inter-annual variability. The SET was closely related to the average annual temperature and station elevation (r = ǀ0.95ǀ–ǀ0.99ǀ), while the corresponding trend relationships were weaker (r = ǀ0.40ǀ–ǀ0.43ǀ). This study provides data on the zonation of the conditions required to achieve specific CC management objectives. Full article

(This article belongs to the Special Issue Evolving Plant Phenology Responses and Resilience in a Changing Climate)

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18 pages, 2828 KiB

Open AccessArticle

The Philippines’ Energy Transition: Assessing Emerging Technology Options Using OSeMOSYS (Open-Source Energy Modelling System)

by Lara Dixon, Rudolf Yeganyan, Naomi Tan, Carla Cannone, Mark Howells, Vivien Foster and Fernando Plazas-Niño

Climate 2025, 13(1), 14; https://doi.org/10.3390/cli13010014 - 8 Jan 2025

Abstract

The Philippines aspires for a clean energy future but has become increasingly reliant on imported fossil fuels due to rising energy demands. Despite renewable energy targets and a coal moratorium, emissions reductions have yet to materialize. This study evaluates the potential of offshore [...] Read more.

The Philippines aspires for a clean energy future but has become increasingly reliant on imported fossil fuels due to rising energy demands. Despite renewable energy targets and a coal moratorium, emissions reductions have yet to materialize. This study evaluates the potential of offshore wind (floating and fixed), floating solar PV, in-stream tidal, and nuclear power to contribute to a Net-Zero energy plan for the Philippines, utilizing the Open-Source Energy Modelling System (OSeMOSYS). Seven scenarios were analyzed, including least-cost, renewable energy targets; Net-Zero emissions; and variations in offshore wind growth and nuclear power integration. Floating solar PV and offshore wind emerged as key decarbonization technologies, with uptake in all scenarios. Achieving Net-Zero CO₂ emissions by 2050 proved technically feasible but requires substantial capital, particularly after 2037. Current renewable energy targets are inadequate to induce emissions reductions; and a higher target of ~42% by 2035 was found to be more cost-effective. The addition of nuclear power showed limited cost and emissions benefits. Emissions reductions were projected to mainly occur after 2038, highlighting the need for more immediate policy action. Recommendations include setting a higher renewables target, offshore wind capacity goals, a roadmap for floating solar PV, and better incentives for private investment in renewables and electric transport. Full article

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14 pages, 3279 KiB

Open AccessArticle

A Pilot Study on the Main Air Pollutants in a Rural Community in Guanajuato, Mexico, Using a Low-Cost ATMOTUBE^® Monitor

by Rebeca Monroy-Torres

Climate 2025, 13(1), 13; https://doi.org/10.3390/cli13010013 - 8 Jan 2025

Abstract

Air pollution is the second leading cause of death from non-communicable diseases. In Guanajuato, Mexico, the brick industry is the main economic source of polluting emissions, with the greatest health impacts. This sector has initiated government regulatory changes, but there is currently no [...] Read more.

Air pollution is the second leading cause of death from non-communicable diseases. In Guanajuato, Mexico, the brick industry is the main economic source of polluting emissions, with the greatest health impacts. This sector has initiated government regulatory changes, but there is currently no monitoring of its impact on health. As a first pilot phase, this study’s objective was to measure the main air pollutants in a rural community in Guanajuato, Mexico, using a low-cost ATMOTUBE^® monitor and to describe the area and population group at the greatest risk of exposure. An analytical and longitudinal design from September 2023 to February 2024, with the ATMOTUBE^® measurement parameters VOC, PM₁, PM_2.5, PM₁₀, temperature, humidity, and pressure, was used. During the six months of measurement, the results were as follows: a VOC of 4.15 ± 11.79 ppm, an Air Quality Score (AQS) of 65.17 ± 30.11, and a PM₁ value of 4.90 ± 18.43 μg/m³. January–February 2024 was the period with the highest concentration of pollutants, with a maximum PM_2.5 concentration of 664 ± 12.5 μg/m³, a maximum PM₁₀ concentration of 650 ± 14.8 μg/m³, and a low humidity value (34.1 ± 5.2%). These values were found near two schools. The first inventory of the main air pollutants in this rural community is presented, with children and women being the population at greatest risk. With these data from this pilot phase, it is recommended to start implementing surveillance measures alongside health and nutrition indicators, mainly for the vulnerable population of this rural community. Full article

(This article belongs to the Topic Advances in Low-Carbon, Climate-Resilient, and Sustainable Built Environment)

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29 pages, 5449 KiB

Open AccessArticle

Assessment of Climate Change in Angola and Potential Impacts on Agriculture

by Carlos D. N. Correia, Malik Amraoui and João A. Santos

Climate 2025, 13(1), 12; https://doi.org/10.3390/cli13010012 - 7 Jan 2025

Abstract

Agroclimatic indicators help convey information about climate variability and change in terms that are meaningful to the agricultural sector. This study evaluated climate projections for Angola, particularly for provinces with more significant agricultural potential. To this end, 15 predefined agroclimatic indicators in 2041–2070 [...] Read more.

Agroclimatic indicators help convey information about climate variability and change in terms that are meaningful to the agricultural sector. This study evaluated climate projections for Angola, particularly for provinces with more significant agricultural potential. To this end, 15 predefined agroclimatic indicators in 2041–2070 and 2071–2099, under the anthropogenic forcing scenarios RCP4.5 and RCP8.5, were compared with the historical period 1981–2010 as a baseline. We selected two climate scenarios and two temporal horizons to obtain a comprehensive view of the potential impacts of climate change in Angola. Data were extracted within the geographic window of longitudes 10–24° E and latitudes 4–18° S and from five general circulation models (GCM), namely MIROC-ESM-CHEM, HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, and NorESM1-M. The set averages of agroclimatic indicators and their differences between historical and future periods are discussed in relation to the likely implications for agriculture in Angola. The results show significant increases in average daily maximum (2–3 °C) and minimum (2–3 °C) temperatures in Angola. For the future, a generally significant reduction in precipitation (and its associated indicators) is expected in all areas of Angola, with the southwest region (Namibe and Huíla) recording the most pronounced decrease, up to 300 mm. At the same time, the maximum number of consecutive dry days will increase across the country, especially in the Northeast. A widespread increase in temperatures is expected, leading to hot and dry conditions in Angola that could lead to more frequent, intense, and prolonged extreme events, such as tropical nights, the maximum number of consecutive summer days, hot and rainy days, and warm period duration index periods. These changes can seriously affect agriculture, water resources, and ecosystems in Angola, thereby requiring adaptation strategies to reduce risks and adverse effects while ensuring the sustainability of the country’s natural resources and guaranteeing its food security. Full article

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