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- Climate, Vol. 13, Pages 43: Feedback Trends with ECS from Energy Rates:
Feedback Doubling and the Vital Need for Solar Geoengineering
Authors: Alec Feinberg
First page: 43
Abstract: This paper provides climate feedback trends, quantifies the feedback-doubling (FD) period, considers urbanization influences, and provides related equilibrium climate sensitivity (ECS) estimates using data from 1880 to 2024. Data modeling is accomplished by focusing on statistically significant stable normalized correlated rates (NCRs, i.e., normalized related slopes). Estimates indicate that the global warming NCR is increasing by a factor of 1.65 to 2.33 times faster than the energy consumption NCR, from 1975 to 2024. The reason is feedback amplification. This is supported by the fact that the NCR for forcing and energy consumption shows approximate equivalency in the period studied. Results provide feedback yearly trend estimates at the 95% confidence level that key results will fall within the IPCC AR6 likely range. The projected 2017–2024 feedback amplification estimates, using the EC approach, range from 2.0 to 2.16, respectively. A feedback amplification of 2.0 (approximately equal to −2.74 Wm−2 K−1) doubles the forcing, indicating that in 2024, more than half of global warming (53.7%) is likely due to feedback. Relative to the feedback-doubling (FD) threshold (i.e., the point where feedback exceeds forcing), the FD overage is 3.7% in 2024. This is the amount of feedback exceeding the forcing portion found to have a surprisingly aggressive 3.1% to 3.9% estimated overage growth rate per decade. We now ask, shouldn’t we try to mitigate feedback as well as GHG forcing, and if forcing could be removed, would global warming fully “self-mitigate”' Additionally, CO2 yearly increases are complex, with poor reduction progress. Therefore, this study’s risk assessment urgently recommends that supplementary “mild” annual solar geoengineering is necessary, to reduce the dominant aggressive feedback. SG reduces the primary solar warming source creating 62% higher mitigation efficiency than CDR. Urgency is enhanced since solar geoengineering must be timely and can take years to develop. This study also estimates that 75% to 90.5% (83% average) of the feedback problem is due to water vapor feedback (WVF). High WVF also plagues many cities needing local SG. Trend analysis indicates that by 2047, the earliest we may reach 10 billion people, feedback amplification could reach a value of 2.4 to 2.8. Furthermore, by 2082, the year estimated for 2× CO2, at the current rate, feedback amplification could range from 2.88 to 3.71. This yields an ECS range from 2.4 °C to 3.07 °C, in reasonable agreement with the reported estimated range in AR6. An overview of recent urbanization forcing attribution indicates the ECS value may be lower by 10.7% if this forcing is considered. For numerous reasons, the lack of albedo urbanization Earth brightening requirements in the Paris Agreement, is unsettling. In addition, a model assesses effective forced feedback (EFF) temperature characteristics of up to 1.9 °C, providing interesting feedback insights that may relate to high GW land and pipeline temperature estimates. Lastly in addition to urbanization, solar geoengineering in the Arctic and Antarctic is advised. Worldwide efforts in GHG mitigation, with no significant work in SG, appears highly misdirected.
Citation: Climate
PubDate: 2025-02-21
DOI: 10.3390/cli13030043
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 44: Diel Variation in Summer Stream Temperature in
an Idaho Desert Stream and Implications for Identifying Thermal Refuges
Authors: Mel Campbell, Donna Delparte, Matthew Belt, Zhongqi Chen, Christopher C. Caudill, Trevor Caughlin
First page: 44
Abstract: Thermal refuges in streams are essential for the survival of coldwater fish species such as Redband trout (Oncorhynchus mykiss) in landscapes with stressful or lethal stream temperatures. We utilized an uncrewed aerial system (UAS) mounted with thermal and natural color sensors to conduct hourly flights over a 24 h period in the desert stream Little Jacks Creek during late summer when temperatures were near seasonal maximums and streamflow was near seasonal minimums. We used fine-resolution imagery to map stream temperatures and characterize how our thermal sensor exhibits variability across a diel period in an environment where thermal sensor viability had not yet been assessed. Thermal imagery from 3 out of 24 flights showed no significant differences when compared to true water temperatures from in-stream temperature loggers, which appeared to be highly dependent on atmospheric conditions. The thermal imagery (range of 9.17 to 21.04 °C) consistently underestimated HOBO logger stream temperatures (range of 13.6 to 17.1 °C) during cooler, nighttime flights and overestimated temperatures during hotter, afternoon hours, resulting in a global RMSE of 2.12 °C. Between-flight RMSE values ranged from 0.53 °C to 4.00 °C, within the error range of the thermal sensor. The thermal data support existing findings of optimal hours for flying UAS thermal surveys and showed specific patterns in TIR sensor accuracy that were dependent on the time of flight. This study yields valuable lessons for future stream temperature data collection in environments with highly variable temperatures, aiding in the calibration of thermal sensors on UAS missions. Furthermore, our results provide insights into environmental stressors such as increased stream temperatures, which is vital for conservation efforts for organisms that rely on coldwater refuges within desert streams.
Citation: Climate
PubDate: 2025-02-22
DOI: 10.3390/cli13030044
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 45: Clean Air Benefits and Climate Penalty: A
Health Impact Analysis of Mortality Trends in the Mid-South Region, USA
Authors: Chunrong Jia, Hongmei Zhang, Namuun Batbaatar, Abu Mohd Naser, Ying Li, Ilias Kavouras
First page: 45
Abstract: The lowering air pollution in the US has brought significant health benefits; however, climate change may offset the benefits by increasing the temperature and worsening air quality. This study aimed to estimate the mortality changes due to air pollution reductions and evaluate the potential climate penalty in the Mid-South Region of the US. Daily concentrations of PM2.5 and ozone measured at local monitoring stations in 1999–2019 were extracted from the US Environmental Protection Agency’s Air Quality System. Meteorological data for the same period were obtained from the National Oceanic and Atmospheric Administration’s Local Climatological Data. Annual average age-adjusted all-cause mortality rates (MRs) were downloaded from the US Centers for Disease Control and Prevention’s WONDERS Databases. MRs attributable to exposure to PM2.5, ozone, and high temperatures in warm months were estimated using their corresponding health impact functions. Using Year 1999 as the baseline, contributions of environmental changes to MR reductions were calculated. Results showed that annual average concentrations of PM2.5 and ozone decreased by 46% and 23% in 2019, respectively, compared with the base year; meanwhile, the mean daily temperature in the warm season fluctuated and displayed an insignificant increasing trend (Kendall’s tau = 0.16, p = 0.30). MRs displayed a significant decreasing trend and dropped by 215 deaths/100,000 person-year in 2019. Lower PM2.5 and ozone concentrations were estimated to reduce 59 and 30 deaths/100,000 person-year, respectively, contributing to 23% and 17% of MR reductions, respectively. The fluctuating temperatures had negligible impacts on mortality changes over the two-decade study period. This study suggests that improved air quality may have contributed to mortality reductions, while the climate penalty effects appeared to be insignificant in the Mid-South Region.
Citation: Climate
PubDate: 2025-02-22
DOI: 10.3390/cli13030045
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 46: A Convection-Permitting Regional Climate
Simulation of Changes in Precipitation and Snowpack in a Warmer Climate
over the Interior Western United States
Authors: Yonggang Wang, Bart Geerts, Changhai Liu, Xiaoqin Jing
First page: 46
Abstract: This study investigates the impacts of climate change on precipitation and snowpack in the interior western United States (IWUS) using two sets of convection-permitting Weather Research and Forecasting model simulations. One simulation represents the ~1990 climate, and another represents an ~2050 climate using a pseudo-global warming approach. Climate perturbations for the future climate are given by the CMIP5 ensemble-mean global climate models under the high-end emission scenario. The study analyzes the projected changes in spatial patterns of seasonal precipitation and snowpack, with particular emphasis on the effects of elevation on orographic precipitation and snowpack changes in four key mountain ranges: the Montana Rockies, Greater Yellowstone area, Wasatch Range, and Colorado Rockies. The IWUS simulations reveal an increase in annual precipitation across the majority of the IWUS in this warmer climate, driven by more frequent heavy to extreme precipitation events. Winter precipitation is projected to increase across the domain, while summer precipitation is expected to decrease, particularly in the High Plains. Snow-to-precipitation ratios and snow water equivalent are expected to decrease, especially at lower elevations, while snowpack melt is projected to occur earlier by up to 26 days in the ~2050 climate, highlighting significant impacts on regional water resources and hydrological management.
Citation: Climate
PubDate: 2025-02-24
DOI: 10.3390/cli13030046
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 47: Projected Drought Intensification in the
Büyük Menderes Basin Under CMIP6 Climate Scenarios
Authors: Farzad Rotbeei, Mustafa Nuri Balov, Mir Jafar Sadegh Safari, Babak Vaheddoost
First page: 47
Abstract: The amplitude and interval of drought events are expected to enhance in upcoming years resulting from global warming and climate alterations. Understanding future drought events’ potential impacts is important for effective regional adaptation and mitigation approaches. The main goal of this research is to study the impacts of climate change on drought in the Büyük Menderes Basin located in the Aegean region of western Türkiye by using the outcomes of three general circulation models (GCMs) from CMIP6 considering two different emission scenarios (SSP2-4.5 and SSP5-8.5). Following a bias correction using a linear scaling method, daily precipitation and temperature projections are used to compute the Standardized Precipitation Evapotranspiration Index (SPEI). The effectiveness of the GCMs in projecting precipitation and temperature is evaluated using observational data from the reference period (1985–2014). Future drought conditions are then assessed based on drought indices for three periods: 2015–2040 (near future), 2041–2070 (mid-term future), and 2071–2100 (late future). Consequently, the number of dry months is projected and expected to elevate, informed by SSP2-4.5 and SSP5-8.5 scenarios, during the late-century timeframe (2071–2100) in comparison to the baseline period (1985–2014). The findings of this study offer an important understanding for crafting adaptation strategies aimed at reducing future drought impacts in the Büyük Menderes Basin in the face of changing climate conditions.
Citation: Climate
PubDate: 2025-02-26
DOI: 10.3390/cli13030047
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 48: Examining Recent Climate Changes in Ghana and
a Comparison with Local Malaria Case Rates
Authors: Ekuwa Adade, Steven Smith, Andrew Russell
First page: 48
Abstract: This study investigated recent climate changes in Ghana and compared these changes to a new malaria case rates dataset for 2008–2022. The analysis was implemented at three spatial scales: national, regional, and by ‘climate zone’ (i.e., coastal, savannah, and forest zones). Descriptive statistics, qualitative discussion and correlation analysis were used to compare the climate variability to the malaria case rates. The climate analysis identified a general warming over the period with a mid-2010s maximum temperature peak in the forest and savannah zones, also associated with changes in the annual temperature cycle. Malaria case rates increased between 2008 and 2013, decreased sharply in 2014, and then decreased steadily from 2015 to 2022 for all scales. The sharp decline was broadly coincident with a change in the temperature regime that would provide a less favourable environment for the malaria vectors (precipitation and humidity showed no comparable changes). These coincident changes were particularly noticeable for an increase in maximum temperatures in the savannah and coastal zones in the key malaria transmission months after 2014. Correlation analysis showed statistically significant (p < 0.05) relationships between malaria case rates and mean and maximum temperatures at the national scale, and malaria case rates and mean, maximum, and minimum temperatures for the coastal climate zone (precipitation and humidity showed no significant correlations). However, more sophisticated methods are required to further understand this multidimensional system.
Citation: Climate
PubDate: 2025-02-27
DOI: 10.3390/cli13030048
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 49: From Optimism to Risk: The Impact of Climate
Change on Temperature Sums in Central Europe
Authors: Martin Minárik, Vladimír Kišš, Agnieszka Ziernicka-Wojtaszek, Martin Prčík, Ján Čimo, Katarína Mikulová
First page: 49
Abstract: This study examines the impact of climate change on agricultural productivity in Slovakia, the Czech Republic, and Poland, focusing on temperature sums influencing the growing season. Using meteorological data from 2001 to 2020, the research analyses the onset and termination of temperatures ≥5 °C (growing season). Temperature sums for two periods (2001–2010, 2011–2020) were calculated and future temperature projections under three scenarios (+1.5 °C, +2.6 °C, +3.6 °C) were developed. Results indicate regional variation in temperature sums, with 69% of the area falling in the 2900–3100 °C range, and Poland showing the highest percentage (81%). In the second decade of the 21st century, temperature sums shifted to the 3100–3300 °C range, affecting 63% of the region. The projections indicate a substantial increase in temperature sums, with the most optimistic scenario (+1.5 °C) leading to the dominance of the 3700–3900 °C range. The warmest areas (West Pannonian Basin), show a temperature sum of 4900–5100 °C. The comparison of predicted and observed temperature sums for 2011–2020 shows a minimal error (±3% in Slovakia and ±4% in Poland and the Czech Republic), confirming the projections. These findings highlight the importance of adaptive strategies in agriculture, particularly fruit farming, to mitigate the climate change effects.
Citation: Climate
PubDate: 2025-02-28
DOI: 10.3390/cli13030049
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 50: Coalition Formation with Cooperation-Enhancing
Transfers When Players Are Heterogeneous and Inequality-Averse
Authors: Marco Rogna, Carla Vogt
First page: 50
Abstract: Obtaining significant levels of cooperation in public goods and environmental games, under the assumption of players being purely selfish, is usually prevented by the problem of free riding. Coalitions, in fact, generally fail to be internally stable, and this causes a serious under-provision of the public good, together with a significant welfare loss. The assumption of relational preferences, capable of better explaining economic behaviours in laboratory experiments, helps to foster cooperation, but, without adequate transfer scheme, no substantial improvements are reached. The present paper proposes a cooperation-enhancing transfer scheme under the assumption of players having Fehr and Schmidt utility functions, whose objectives are to guarantee internal stability and to maximize the sum of the utilities of coalition members. The transfer scheme is tested on a public goods contribution game parameterized on the data provided by the RICE model and benchmarked with other popular transfer schemes in environmental economics. The proposed scheme outperforms its benchmarking counterparts in stabilizing coalitions, and sensibly increases cooperation compared to the absence of transfers. Furthermore, for high but not extreme values of the parameter governing the intensity of dis-utility from disadvantageous inequality, it manages to support very large coalitions.
Citation: Climate
PubDate: 2025-02-28
DOI: 10.3390/cli13030050
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 51: Integrating Land Use/Land Cover and Climate
Change Projections to Assess Future Hydrological Responses: A CMIP6-Based
Multi-Scenario Approach in the Omo–Gibe River Basin, Ethiopia
Authors: Paulos Lukas, Assefa M. Melesse, Tadesse Tujuba Kenea
First page: 51
Abstract: It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin.
Citation: Climate
PubDate: 2025-02-28
DOI: 10.3390/cli13030051
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 52: Wither Adaptation Action
Authors: Janet Stanley, Michael Spencer
First page: 52
Abstract: Longitudinal research commenced in 2012 and was repeated in 2022 in two regional areas in Victoria, Australia. The researchers sought to understand the facilitators and barriers to climate adaptation, given the perception of the authors that climate adaptation was making little progress, a view supported following an extensive literature review and international consultations. Adaptation was not part of the debate when climate change was first discussed by the UN General Assembly in 1988 and not identified by the IPCC until 2007. Recent Australian governments have shown a ‘hands-off’ and uniformed approach. Research workshops and consultations sought the views of residents, community organisations, local governments and representatives of state agencies, who were invited or requested attendance. The workshops were designed to understand the perspective of participants, using a Search Conference methodology with both guided questions and participant-led issues. The results suggest that, despite the presence of many adaptation plans, the fundamental arrangements needed for the scale of adaptation required were not in place in 2012, nor in 2022. There was a lack of federal and state government action beyond their own institutional structures, responsibility for action being passed down the line to local government, business and community. Yet this devolvement was commonly not accompanied by financial support, supportive and inclusive governance arrangements, expert advice, data, or clear guidance for action. Climate adaptation policy remains disconnected from the broader economy, with little progress on how to achieve this task, which is rapidly growing in size and complexity. There is not an accepted roadmap for effective adaptation, an approach that does not easily fit into the risk-averse approach of public sector management that has prevailed in Australia since the 1980s.
Citation: Climate
PubDate: 2025-03-03
DOI: 10.3390/cli13030052
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 53: Analysis of the Relationship Between
Microclimate and Building Energy Loads Based on Apartment Complex Layout
Types
Authors: Sumin Lee, Sukjin Jung, Seonghwan Yoon
First page: 53
Abstract: This study provides fundamental data for optimal planning by analyzing key factors influencing microclimate and building energy loads. * to provide fundamental data for optimal planning. A total of 11 apartment layout types, including tower-type, flat-type, and mixed-type configurations, were analyzed using ENVI-met simulations. The results indicate that layout types significantly influence microclimate and energy consumption. Tower-type layouts enhanced wind flow, reducing surface temperatures and cooling loads. In contrast, dense flat-type layouts restricted airflow, leading to heat accumulation and increased cooling energy demand. Mixed layouts exhibited varied effects depending on the proportion of open spaces and high-density clusters. Additionally, south-facing layouts optimized solar radiation, reducing heating loads, whereas east–west-facing layouts experienced imbalanced solar exposure, increasing cooling demand by 15–20% in the summer. Horizontal parallel and staggered layouts improved ventilation efficiency and mitigated heat accumulation, making them effective strategies for enhancing microclimate and reducing energy consumption. This study confirms that apartment layout planning plays a crucial role in microclimate regulation and energy efficiency. The findings can guide architectural strategies to improve thermal comfort and reduce building energy consumption.
Citation: Climate
PubDate: 2025-03-03
DOI: 10.3390/cli13030053
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 54: Long-Term and Seasonal Analysis of Storm-Wave
Events in the Gulf of California
Authors: Cuauhtémoc Franco-Ochoa, Yedid Guadalupe Zambrano-Medina, Sergio Alberto Monjardin-Armenta, Sergio Arturo Rentería-Guevara
First page: 54
Abstract: Coastal zones are threatened by extreme meteorological phenomena such as storm–wave events. Understanding storm-wave events is essential for sustainable coastal management. This study analyzed the temporal variability (both long-term and seasonal) of the frequency and energy content of storm-wave events in the Gulf of California for the period 1980–2020 using storm-wave data from the fifth-generation climate reanalysis dataset (ERA5). The results indicate that storm events in the Gulf of California are becoming more frequent and energetic. Storm-wave events coming from the north are more frequent but less energetic than those coming from the south. Throughout the year, storm-wave events from both the north and south show seasonal behavior. This paper aims to enhance the understanding of storm-wave events in the Gulf of California and serve as a foundation for future studies, such as coastal impact assessments.
Citation: Climate
PubDate: 2025-03-04
DOI: 10.3390/cli13030054
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 55: The Social Acceptance of Renewable Energy
Communities: The Role of Socio-Political Control and Impure Altruism
Authors: Marialuisa Menegatto, Andrea Bobbio, Gloria Freschi, Adriano Zamperini
First page: 55
Abstract: The ever-worsening climate crisis necessitates a shift toward sustainable energy systems that prioritise citizen participation. Renewable Energy Communities (RECs) present a unique opportunity to enhance local resilience, reduce greenhouse gas emissions, and foster climate mitigation and adaptation through participatory governance. This exploratory study investigates the psychosocial predictors of social acceptance for RECs, with a focus on Socio-political Control and Warm-glow Motivation as key determinants. To this end, we collected 107 questionnaires completed by residents of the metropolitan city of Padua, which is engaged in the EU’s 100 Climate-Neutral Cities by 2030 mission. The results indicate a generally favourable attitude toward RECs and reveal that Socio-political Control, defined as the perceived ability to influence societal and political systems, positively predicts community energy acceptance. Furthermore, Impure Altruism (Warm-glow Motivation) mediates this relationship, underscoring the importance of intrinsic emotional rewards in fostering support for sustainable energy projects. These findings highlight the interplay between individual agency and emotional satisfaction in promoting energy transitions. This study underscores the need for participatory governance and tailored communication strategies to enhance public engagement with RECs. Limitations and avenues for future research are discussed, emphasising the need for broader cross-cultural investigations and experimental designs.
Citation: Climate
PubDate: 2025-03-06
DOI: 10.3390/cli13030055
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 56: Addressing the Impact of Complex English Use
in Communicating Climate Change in Nigerian Communities Through Contextual
Understanding
Authors: Chinwe P. Oramah, Tochukwu A. Ngwu, Chinwe Ngozi Odimegwu
First page: 56
Abstract: The effective implementation of preparedness and response strategies toward climate change resilience has evolved into a technical, sociopolitical, and communication issue. We argue that, for climate communication to effectively contribute to community resilience, it demands meaningful dialogue and engagement to facilitate understanding. Using the risk communication theory, we assessed the impact of complex English language on climate change understanding in Nigerian communities where local languages are predominant. Through surveys and semi-structured interviews, we found that current communication strategies are ineffective and misaligned with the local context, traditional knowledge systems, and specific community concerns, therefore marginalizing local actors from meaningful participation. The translation of climate communication into climate change action is challenging for local actors due to prevailing exclusion from discussion and a lack of engagement, which contributes to misunderstanding and poor climate change action. The study indicates that enhancing climate change communication in Nigeria necessitates the development of integrative strategies tailored to the language, cultural, and educational context that will encourage the local actors to participate effectively in this discussion. The paper recommends translating information into local languages and integrating local proverbs and mythological interpretations that can be positively employed to combat climate change within these communities more organically.
Citation: Climate
PubDate: 2025-03-09
DOI: 10.3390/cli13030056
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 57: Climate Change and Its Impact on Natural
Resources and Rural Livelihoods: Gendered Perspectives from Naryn,
Kyrgyzstan
Authors: Azamat Azarov, Maksim Kulikov, Roy C. Sidle, Vitalii Zaginaev
First page: 57
Abstract: Climate change poses significant threats to rural communities in Kyrgyzstan, particularly for agriculture, which relies heavily on natural resources. In Naryn Province, rising temperatures and increasing natural hazards amplify vulnerabilities, especially in high mountain areas. Addressing these challenges requires understanding both environmental factors and the perceptions of affected communities, as these shape adaptive responses. This study enhances understanding of climate change impacts on communities in Naryn Province by combining environmental and social assessments through a gendered lens, with a particular focus on women. Environmental data, including air temperature, precipitation, river discharge, and satellite-derived vegetation indices, were analyzed to evaluate changes in vegetation and water resources. Social data were collected through interviews with 298 respondents (148 women and 150 men) across villages along the Naryn River, with chi-square analysis used to examine gender-specific perceptions and impacts on livelihoods. The results indicated a noticeable rise in temperatures and a slight decline in precipitation over recent decades, affecting vegetation and grazing areas near settlements. While respondents of both genders reported similar observations, differences emerged in how changes affect their roles and activities, with localized variations linked to household and agricultural responsibilities. The findings highlight the need for inclusive adaptation strategies that address diverse experiences and priorities, providing a foundation for equitable and effective climate resilience measures.
Citation: Climate
PubDate: 2025-03-10
DOI: 10.3390/cli13030057
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 58: Variation in the Extreme Temperatures and
Related Climate Indices for the Marche Region, Italy
Authors: Luciano Soldini, Giovanna Darvini
First page: 58
Abstract: This paper presents a study on the evolution of extreme temperatures in the Marche region, Central Italy. To this end, a complete dataset compiled using data collected from available thermometric stations over the years 1957–2019 based on minimum and maximum daily temperatures was selected. The yearly mean values of extreme temperature and relative climate indices defined by the Expert Team on Climate Change Detection and Indices were calculated, and a trend analysis was performed. The spatial distribution of the trends was assessed, and the variations in extreme temperatures in the medium–long term were considered by calculating mean values with respect to different climatological standard normals and decades. The analyzed parameters show that extreme heat events characterized by increasing intensity and frequency have occurred over the years, while cold weather events have decreased. A high percentage of stations recorded an increase in all indices related to daily maximum temperatures, and a simultaneous decline of those related to daily minimum values, under both nighttime and daytime conditions. This phenomenon characterizes the entire Marche region. A detailed analysis of the heat wave indices confirms an increasing trend, with a notable increase beginning in the early 1980s.
Citation: Climate
PubDate: 2025-03-10
DOI: 10.3390/cli13030058
Issue No: Vol. 13, No. 3 (2025)
- Climate, Vol. 13, Pages 21: Daily Concentration of Precipitation in the
Province of Alicante (1981–2020)
Authors: Esther Sánchez-Almodóvar, Jorge Olcina-Cantos, Javier Martin-Vide, Javier Martí-Talavera
First page: 21
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 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.
Citation: Climate
PubDate: 2025-01-22
DOI: 10.3390/cli13020021
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 22: The Influence of Climate Variables on Malaria
Incidence in Vanuatu
Authors: Jade Sorenson, Andrew B. Watkins, Yuriy Kuleshov
First page: 22
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 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.
Citation: Climate
PubDate: 2025-01-22
DOI: 10.3390/cli13020022
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 23: Predicting Asthma Hospitalizations from
Climate and Air Pollution Data: A Machine Learning-Based Approach
Authors: 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, Helber Barros Gomes
First page: 23
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 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.
Citation: Climate
PubDate: 2025-01-24
DOI: 10.3390/cli13020023
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 24: Sub-Daily Performance of a
Convection-Permitting Model in Simulating Decade-Long Precipitation over
Northwestern Türkiye
Authors: Cemre Yürük Sonuç, Veli Yavuz, Yurdanur Ünal
First page: 24
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 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.
Citation: Climate
PubDate: 2025-01-24
DOI: 10.3390/cli13020024
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 25: El Niño–Southern Oscillation
Prediction Based on the Global Atmospheric Oscillation in CMIP6 Models
Authors: Ilya V. Serykh
First page: 25
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 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.
Citation: Climate
PubDate: 2025-01-27
DOI: 10.3390/cli13020025
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 26: Tree Crown Damage and Physiological Responses
Under Extreme Heatwave in Heterogeneous Urban Habitat of Central China
Authors: Li Zhang, Wenli Zhu, Ming Zhang, Xiaoyi Xing
First page: 26
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) 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.
Citation: Climate
PubDate: 2025-01-28
DOI: 10.3390/cli13020026
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 27: Climate Change Worry in German University
Students: Determinants and Associations with Health-Related Outcomes
Authors: Andrea Söder, Raphael M. Herr, Tatiana Görig, Katharina Diehl
First page: 27
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 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.
Citation: Climate
PubDate: 2025-01-29
DOI: 10.3390/cli13020027
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 28: Snow Resources and Climatic Variability in
Jammu and Kashmir, India
Authors: Aaqib Ashraf Bhat, Poul Durga Dhondiram, Saurabh Kumar Gupta, Shruti Kanga, Suraj Kumar Singh, Gowhar Meraj, Pankaj Kumar, Bhartendu Sajan
First page: 28
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 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.
Citation: Climate
PubDate: 2025-01-30
DOI: 10.3390/cli13020028
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 29: Preparedness, Response, and Communication
Preferences of Dairy Farmers During Extreme Weather Events: A
Phenomenological Case Study
Authors: Emmanuel C. Okolo, Rafael Landaverde, David Doerfert, Juan Manuel Piñeiro, Darren Hudson, Chanda Elbert, Kelsi Opat
First page: 29
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 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.
Citation: Climate
PubDate: 2025-01-31
DOI: 10.3390/cli13020029
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 30: Assessing Green Strategies for Urban Cooling
in the Development of Nusantara Capital City, Indonesia
Authors: Radyan Putra Pradana, Vinayak Bhanage, Faiz Rohman Fajary, Wahidullah Hussainzada, Mochamad Riam Badriana, Han Soo Lee, Tetsu Kubota, Hideyo Nimiya, I Dewa Gede Arya Putra
First page: 30
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 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.
Citation: Climate
PubDate: 2025-01-31
DOI: 10.3390/cli13020030
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 31: Next-Generation Drought
Intensity–Duration–Frequency Curves for Early Warning Systems
in Ethiopia’s Pastoral Region
Authors: Getachew Tegegne, Sintayehu Alemayehu, Sintayehu W. Dejene, Liyuneh Gebre, Tadesse Terefe Zeleke, Lidya Tesfaye, Numery Abdulhamid
First page: 31
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 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.
Citation: Climate
PubDate: 2025-02-02
DOI: 10.3390/cli13020031
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 32: A Systematic Review of Effective Measures to
Resist Manipulative Information About Climate Change on Social Media
Authors: Aliaksandr Herasimenka, Xianlingchen Wang, Ralph Schroeder
First page: 32
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 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.
Citation: Climate
PubDate: 2025-02-05
DOI: 10.3390/cli13020032
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 33: Soybean Yield Modeling and Analysis with
Weather Dynamics in the Greater Mississippi River Basin
Authors: Weiwei Xie, Yanbo Huang, Qingmin Meng
First page: 33
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 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.
Citation: Climate
PubDate: 2025-02-06
DOI: 10.3390/cli13020033
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 34: Snow Cover and Depth Climatology and Trends in
Greece
Authors: Ioannis Masloumidis, Stavros Dafis, George Kyros, Konstantinos Lagouvardos, Vassiliki Kotroni
First page: 34
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 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.
Citation: Climate
PubDate: 2025-02-06
DOI: 10.3390/cli13020034
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 35: Climate Change and Arbovirus: A Review and
Bibliometric Analysis
Authors: Maryly Weyll Sant’Anna, Maurício Lamano Ferreira, Leonardo Ferreira da Silva, Pedro Luiz Côrtes
First page: 35
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 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.
Citation: Climate
PubDate: 2025-02-06
DOI: 10.3390/cli13020035
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 36: Future Projections of Clouds and Precipitation
Patterns in South Asia: Insights from CMIP6 Multi-Model Ensemble Under
SSP5 Scenarios
Authors: Praneta Khardekar, Rohini Lakshman Bhawar, Vinay Kumar, Hemantkumar S. Chaudhari
First page: 36
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 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.
Citation: Climate
PubDate: 2025-02-08
DOI: 10.3390/cli13020036
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 37: Scaling Properties of Rainfall as a Basis for
Intensity–Duration–Frequency Relationships and Their Spatial
Distribution in Catalunya, NE Spain
Authors: María del Carmen Casas-Castillo, Alba Llabrés-Brustenga, Raül Rodríguez-Solà, Anna Rius, Àngel Redaño
First page: 37
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 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.
Citation: Climate
PubDate: 2025-02-08
DOI: 10.3390/cli13020037
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 38: The Relationship Between the Occurrence of
Fires and Family Farming in Municipalities in the State of São
Paulo, Brazil
Authors: Leonardo Pinto de Magalhães, Anderson de Souza Gallo, Guilherme Honório Fernandez, Adriana Cavalieri Sais, Renata Evangelista de Oliveira
First page: 38
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 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.
Citation: Climate
PubDate: 2025-02-11
DOI: 10.3390/cli13020038
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 39: Analysis of the Dynamics of Hydroclimatic
Extremes in Urban Areas: The Case of Grand-Nokoué in Benin, West
Africa
Authors: Vidjinnagni Vinasse Ametooyona Azagoun, Kossi Komi, Expédit Wilfrid Vissin, Komi Selom Klassou
First page: 39
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 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.
Citation: Climate
PubDate: 2025-02-12
DOI: 10.3390/cli13020039
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 40: Perceptions of the Barriers to the
Implementation of a Successful Climate Change Policy in Bulgaria
Authors: Antonina Atanasova, Kliment Naydenov
First page: 40
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 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.
Citation: Climate
PubDate: 2025-02-13
DOI: 10.3390/cli13020040
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 41: Expected Impacts on Mediterranean Forest
Species Under Climate Change
Authors: Álvaro Enríquez-de-Salamanca
First page: 41
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 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.
Citation: Climate
PubDate: 2025-02-14
DOI: 10.3390/cli13020041
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 42: Climate Change Exposure of Agriculture Within
Regulated Groundwater Basins of the Southwestern United States
Authors: Lauren E. Parker, Ning Zhang, Isaya Kisekka, John T. Abatzoglou, Emile H. Elias, Caitriana M. Steele, Steven M. Ostoja
First page: 42
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, 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.
Citation: Climate
PubDate: 2025-02-16
DOI: 10.3390/cli13020042
Issue No: Vol. 13, No. 2 (2025)
- Climate, Vol. 13, Pages 7: Identifying Flood Source Areas and Analyzing
High-Flow Extremes Under Changing Land Use, Land Cover, and Climate in the
Gumara Watershed, Upper Blue Nile Basin, Ethiopia
Authors: Haile Belay, Assefa M. Melesse, Getachew Tegegne, Habtamu Tamiru
First page: 7
Abstract: Changes in land use and land cover (LULC) and climate increasingly influence flood occurrences in the Gumara watershed, located in the Upper Blue Nile (UBN) basin of Ethiopia. This study assesses how these factors impact return period-based peak floods, flood source areas, and future high-flow extremes. Merged rainfall data (1981–2019) and ensemble means of four CMIP5 and four CMIP6 models were used for historical (1981–2005), near-future (2031–2055), and far-future (2056–2080) periods under representative concentration pathways (RCP4.5 and RCP8.5) and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). Historical LULC data for the years 1985, 2000, 2010, and 2019 and projected LULC data under business-as-usual (BAU) and governance (GOV) scenarios for the years 2035 and 2065 were used along with rainfall data to analyze flood peaks. Flood simulation was performed using a calibrated Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) model. The unit flood response (UFR) approach ranked eight subwatersheds (W1–W8) by their contribution to peak flood magnitude at the main outlet, while flow duration curves (FDCs) of annual maximum (AM) flow series were used to analyze changes in high-flow extremes. For the observation period, maximum peak flood values of 211.7, 278.5, 359.5, 416.7, and 452.7 m3/s were estimated for 5-, 10-, 25-, 50-, and 100-year return periods, respectively, under the 2019 LULC condition. During this period, subwatersheds W4 and W6 were identified as major flood contributors with high flood index values. These findings highlight the need to prioritize these subwatersheds for targeted interventions to mitigate downstream flooding. In the future period, the highest flow is expected under the SSP5-8.5 (2056–2080) climate scenario combined with the BAU-2065 land use scenario. These findings underscore the importance of strategic land management and climate adaptation measures to reduce future flood risks. The methodology developed in this study, particularly the application of RF-MERGE data in flood studies, offers valuable insights into the existing knowledge base on flood modeling.
Citation: Climate
PubDate: 2025-01-01
DOI: 10.3390/cli13010007
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 8: Assessment of the Long-Term Impact of Climate
Variability on Food Production Systems in South Africa (1976–2020)
Authors: Thulani Ningi, Maremo Mphahlele, Vusimusi Sithole, Jabulile Zamokuhle Manyike, Bernard Manganyi, Saul Ngarava, Moses Herbert Lubinga, Lwazi Dladla, Solly Molepo
First page: 8
Abstract: The global impact of climate variability and change on agricultural production systems is a pressing concern with far-reaching implications. While substantial literature exists on these impacts, there is a notable lack of long-term studies that comprehensively analyse the relationship between climate variables and food production systems in South Africa over extended periods. This study addresses this gap by utilising longitudinal data spanning 45 years (1976–2020) and employing an ordinary least squares regression model for analysis. The findings reveal that temperature has a significant positive effect on animal and horticultural production systems. On marginal variability, a 1 °C increase in annual temperature and precipitation levels leads to an increases in animal production (244.2%), field crops (226.4%), and a decrease in horticultural crops (−116.62%). These results underscore the pronounced effects of climate variability on animal, field, and horticultural production systems. This study concludes that rising temperatures positively influence animal and horticultural production. It recommends prioritising climate-smart agricultural practices to enhance resilience and productivity, particularly in colder seasons. By implementing these strategies, South Africa can strengthen its food production systems, ensuring sustainable agricultural growth in the face of climate variability and change.
Citation: Climate
PubDate: 2025-01-02
DOI: 10.3390/cli13010008
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 9: Tropical Glaciation and Glacio-Epochs: Their
Tectonic Origin in Paleogeography
Authors: Hsien-Wang Ou
First page: 9
Abstract: Precambrian tropical glaciation is an enigma of Earth’s climate. Overlooking fundamental difference of land/sea icelines, it was equated with a global frozen ocean, which is at odds with the sedimentary evidence of an active hydrological cycle, and its genesis via the runaway ice–albedo feedback conflicts with the mostly ice-free Proterozoic when its trigger threshold was well exceeded by the dimmer sun. In view of these shortfalls, I put forth two key hypotheses of the tropical glaciation: first, if seeded by mountain glaciers, the land ice would advance on sea level to be halted by above-freezing summer temperature, which thus abuts an open cozonal ocean; second, a tropical supercontinent would block the brighter tropical sun to cause the required cooling. To test these hypotheses, I formulate a minimal tropical/polar box model to examine the temperature response to a varying tropical land area and show that tropical glaciation is indeed plausible when the landmass is concentrated in the tropics despite uncertain model parameters. In addition, given the chronology of paleogeography, the model may explain the observed deep time climate to provide a unified account of the faint young Sun paradox, Precambrian tropical glaciations, and Phanerozoic glacio-epochs, reinforcing, therefore, the uniformitarian principle.
Citation: Climate
PubDate: 2025-01-02
DOI: 10.3390/cli13010009
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 10: Nexus of Foreign Direct Investment (FDI) and
Environmental Emissions in South Africa: A Markov-Switching Regression
Authors: Teboho Mosikari, Diteboho Xaba
First page: 10
Abstract: The study on the link concerning FDI and environmental emissions has been the interest in recent environmental economics subject. The interest of this research work is to dynamically understand the effect of FDI on environmental emissions in South Africa. The research applied the renowned Markov-switching regression to explore the association among the variables. Prior to the formal estimation, the data were subjected to a linearity test, non-linear unit root test and cusum test so to ascertain whether the variables conform to non-linearity modeling. The results demonstrated that in both regimes (lower or higher emissions), the influence of FDI is positive and statistically significant. This finding implies that foreign investment is detrimental to our environment, irrespective of regime changes. This finding supports the Pollution Haven Hypothesis (PHH). Furthermore, the results show that emissions in South Africa stay in a low or high regime for a short period between one and two years. Policy implications to the results are that economic and climate change policy makers in South Africa should start to regulate FDI to be environmentally friendly.
Citation: Climate
PubDate: 2025-01-03
DOI: 10.3390/cli13010010
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 11: Identifying the Areas at Risk of Huaico
Occurrences in the Department of Lima, Peru
Authors: Geise Macedo dos Santos, Vania Elisabete Schneider, Gisele Cemin, Matheus Poletto
First page: 11
Abstract: Because of local climate, a phenomenon called huaico occurs in the coastal regions of Peru, configured by an alluvial flow of surface runoff caused by precipitation and accompanied by the transport of solid particles. A total of 24% of the huaicos recorded in Peru from 2003 to 2019 were concentrated in the Department of Lima alone and affected 38,000 people. Thus, the aim of this study was to use Maxent to identify the areas at risk of huaicos in this department. To this end, a georeferenced database was created that included the locations of these events for modeling. We used variables suggested by Peru’s Geological, Mining, and Metallurgical Institute (INGEMMET)—geology, geomorphology, DEM, slope, and precipitation—which returned extremely high kappa coefficients. Approximately 42% of Lima’s area is likely to have a huaico occurrence. The most crucial variable for the models was the geomorphological classification characterized by the accumulation of mobilized material, as was the case in previous huaico models. In addition, the monthly approach should have been more effective at determining the differences in the precipitation levels. Thus, new models for the coastal departments of Peru using Maxent algorithms should take a new approach related to precipitation, although the use of Maxent proved satisfactory.
Citation: Climate
PubDate: 2025-01-05
DOI: 10.3390/cli13010011
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 12: Assessment of Climate Change in Angola and
Potential Impacts on Agriculture
Authors: Carlos D. N. Correia, Malik Amraoui, João A. Santos
First page: 12
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 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.
Citation: Climate
PubDate: 2025-01-07
DOI: 10.3390/cli13010012
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 13: A Pilot Study on the Main Air Pollutants in a
Rural Community in Guanajuato, Mexico, Using a Low-Cost ATMOTUBE®
Monitor
Authors: Rebeca Monroy-Torres
First page: 13
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 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, PM2.5, 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 PM2.5 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.
Citation: Climate
PubDate: 2025-01-08
DOI: 10.3390/cli13010013
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 14: The Philippines’ Energy Transition:
Assessing Emerging Technology Options Using OSeMOSYS (Open-Source Energy
Modelling System)
Authors: Lara Dixon, Rudolf Yeganyan, Naomi Tan, Carla Cannone, Mark Howells, Vivien Foster, Fernando Plazas-Niño
First page: 14
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 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.
Citation: Climate
PubDate: 2025-01-08
DOI: 10.3390/cli13010014
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 15: Agroclimatic Zoning of Temperature Limitations
for Growth of Stubble Cover Crops
Authors: Jan Haberle, Filip Chuchma, Ivana Raimanova, Jana Wollnerova
First page: 15
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 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.
Citation: Climate
PubDate: 2025-01-09
DOI: 10.3390/cli13010015
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 16: Using Hybrid Deep Learning Models to Predict
Dust Storm Pathways with Enhanced Accuracy
Authors: Mahdis Yarmohamadi, Ali Asghar Alesheikh, Mohammad Sharif
First page: 16
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 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.
Citation: Climate
PubDate: 2025-01-12
DOI: 10.3390/cli13010016
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 17: 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
Authors: Thatiparthi Koteshwaramma, Kuvar Satya Singh, Sridhara Nayak
First page: 17
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 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.
Citation: Climate
PubDate: 2025-01-13
DOI: 10.3390/cli13010017
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 18: Effect of Short Duration Heat Stress on the
Physiological and Production Parameters of Holstein-Friesian Crossbred
Dairy Cows in Bangladesh
Authors: 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, Md. Morshedur Rahman
First page: 18
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 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 crossbreed 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 crossbreed cows.
Citation: Climate
PubDate: 2025-01-13
DOI: 10.3390/cli13010018
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 19: Projecting Climate Change Impacts on
Benin’s Cereal Production by 2050: A SARIMA and PLS-SEM Analysis of
FAO Data
Authors: Kossivi Fabrice Dossa, Jean-François Bissonnette, Nathalie Barrette, Idiatou Bah, Yann Emmanuel Miassi
First page: 19
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 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.
Citation: Climate
PubDate: 2025-01-16
DOI: 10.3390/cli13010019
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 20: Navigating Global Environmental Challenges:
Disciplinarity, Transdisciplinarity, and the Emergence of Mega-Expertise
Authors: Rolf Lidskog
First page: 20
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 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.
Citation: Climate
PubDate: 2025-01-16
DOI: 10.3390/cli13010020
Issue No: Vol. 13, No. 1 (2025)
- Climate, Vol. 13, Pages 1: Changes in Climate and Their Implications for
Cattle Nutrition and Management
Authors: Bashiri Iddy Muzzo, R. Douglas Ramsey, Juan J. Villalba
First page: 1
Abstract: Climate change is a global challenge that impacts rangeland and pastureland landscapes by inducing shifts in temperature variability, precipitation patterns, and extreme weather events. These changes alter soil and plant conditions, reducing forage availability and chemical composition and leading to nutritional stress in cattle. This stress occurs when animals lack adequate water and feed sources or when these resources are insufficient in quantity, composition, or nutrient balance. Several strategies are essential to address these impacts. Genetic selection, epigenetic biomarkers, and exploration of epigenetic memories present promising avenues for enhancing the resilience of cattle populations and improving adaptation to environmental stresses. Remote sensing and GIS technologies assist in locating wet spots to establish islands of plant diversity and high forage quality for grazing amid ongoing climate change challenges. Establishing islands of functional plant diversity improves forage quality, reduces carbon and nitrogen footprints, and provides essential nutrients and bioactives, thus enhancing cattle health, welfare, and productivity. Real-time GPS collars coupled with accelerometers provide detailed data on cattle movement and activity, aiding livestock nutrition management while mitigating heat stress. Integrating these strategies may offer significant advantages to animals facing a changing world while securing the future of livestock production and the global food system.
Citation: Climate
PubDate: 2024-12-24
DOI: 10.3390/cli13010001
Issue No: Vol. 13, No. 1 (2024)
- Climate, Vol. 13, Pages 2: User-Driven Climate Resilience Across Southern
European Regions
Authors: Georgios Xekalakis, Patricia Molina Lopez, Manuel Argamasilla Ruiz, Tanja Tötzer, Patrick Kaleta, Konstantinos Karystinakis, Anastasia Moumtzidou, Renata Forjan, Petros Christou, Christos Anastasiou, Venera Pavone, Gigliola D’Angelo, Francisco Solano Cobos, Marianne Bügelmayer-Blaschek, Socrates Boutsis, Marija Vurnek, Ivan Murano, Paola Del Prete, Peter Kutschera, Dimitrios Leonidis, Evi Kazamia, Adam Warde, James Hawkes, Pietro Colonna, Vincenzo Petruso, Beniamino Russo, Mattia Federico Leone, Martin Schneider, Andrea Hochebner, Giulio Zuccaro, Denis Havlik
First page: 2
Abstract: This study presents the ClimEmpower framework, a user-driven approach to enhancing climate resilience across five climate-vulnerable regions in Southern Europe: Costa del Sol (Spain), Central Greece, the Troodos Mountains (Cyprus), Osijek-Baranja County (Croatia), and Sicily (Italy). The project employs a region-specific methodology that integrates climate risk assessments, stakeholder engagement through Communities of Practice (CoPs), and the development of innovative climate services tailored to local needs. These regions, characterized by unique environmental and socio-economic vulnerabilities, face shared hazards such as droughts, heatwaves, and floods, alongside region-specific challenges like salinization and biodiversity loss. ClimEmpower identifies critical gaps in high-resolution data, cross-sectoral collaboration, and capacity-building efforts, underscoring barriers to effective adaptation. This work aims to provide a foundational resource, offering a comprehensive overview of the current situation, including needs, gaps, priorities, and expectations across the target regions. By establishing this baseline, it facilitates future research and comparative analyses, contributing to the development of robust, region-specific resilience strategies. The ClimEmpower framework offers scalable and replicable solutions aligned with the European Green Deal’s climate resilience goals, advancing adaptation planning and providing actionable insights for broader European initiatives.
Citation: Climate
PubDate: 2024-12-27
DOI: 10.3390/cli13010002
Issue No: Vol. 13, No. 1 (2024)
- Climate, Vol. 13, Pages 3: Agro-Climatic Zoning of the Territory of
Northern Kazakhstan for Zoning of Agricultural Crops Under Conditions of
Climate Change
Authors: Saken Baisholanov, Kanat Akshalov, Yerbolat Mukanov, Bakytbek Zhumabek, Ergali Karakulov
First page: 3
Abstract: Assessments of the agro-climatic resources of Northern Kazakhstan are urgently needed in the face of climate change and increasing threats to food security in the world, and they can provide valuable information for specialists in the field of agriculture. To assess the agro-climatic conditions of Northern Kazakhstan, the following agro-climatic indices were used: heat availability, moisture availability, and aridity of the growing season for the period 1991–2023. The research results rendered it possible to build maps of the spatial distribution of agro-climatic indicators, and five agro-climatic zones, ranging from “moderately humid moderately warm” in the north to “very arid moderately hot” in the south of Northern Kazakhstan, were identified. Recommendations were developed with respect to the agro-climatic zoning of main crops, taking into account the climatic resources of Northern Kazakhstan. The data obtained will be used for the strategic planning of the agricultural crop industry in Northern Kazakhstan.
Citation: Climate
PubDate: 2024-12-28
DOI: 10.3390/cli13010003
Issue No: Vol. 13, No. 1 (2024)
- Climate, Vol. 13, Pages 4: Assessing NOAA/GFDL Models Performance for
South American Seasonal Climate: Insights from CMIP6 Historical Runs and
Future Projections
Authors: Marília Harumi Shimizu, Juliana Aparecida Anochi, Diego Jatobá Santos
First page: 4
Abstract: Climate prediction is of fundamental importance to various sectors of society and the economy, as it can predict the likelihood of droughts or excessive rainfall in vulnerable regions. Climate models are useful tools in producing reliable climate forecasts, which have become increasingly vital due to the rising impacts of climate change. As global temperatures rise, changes in precipitation patterns are expected, increasing the importance of reliable seasonal forecasts to support planning and adaptation efforts. In this study, we evaluated the performance of NOAA/GFDL models from CMIP6 simulations in representing the climate of South America under three configurations: atmosphere-only, coupled ocean-atmosphere, and Earth system. Our analysis revealed that all three configurations successfully captured key climatic features, such as the South Atlantic Convergence Zone (SACZ), the Bolivian High, and the Intertropical Convergence Zone (ITCZ). However, coupled models exhibited larger errors and lower correlation (below 0.6), particularly over the ocean and the South American Monsoon System, which indicates a poor representation of precipitation compared with atmospheric models. The coupled models also overestimated upward motion linked to the southern Hadley cell during austral summer and underestimated it during winter, whereas the atmosphere-only models more accurately simulated the Walker circulation, showing stronger vertical motion around the Amazon. In contrast, the coupled models simulated stronger upward motion over Northeast Brazil, which is inconsistent with reanalysis data. Moreover, we provided insights into how model biases may evolve under climate change scenarios. Future climate projections for the mid-century period (2030–2060) under the SSP2-4.5 and SSP5-8.5 scenarios indicate significant changes in the global energy balance, with an increase of up to 0.9 W/m2. Additionally, the projections reveal significant warming and drying in most of the continent, particularly during the austral spring, accompanied by increases in sensible heat flux and decreases in latent heat flux. These findings highlight the risk of severe and prolonged droughts in some regions and intensified rainfall in others. By identifying and quantifying the biases inherent in climate models, this study provides insights to enhance seasonal forecasts in South America, ultimately supporting strategic planning, impact assessments, and adaptation strategies in vulnerable regions.
Citation: Climate
PubDate: 2024-12-28
DOI: 10.3390/cli13010004
Issue No: Vol. 13, No. 1 (2024)
- Climate, Vol. 13, Pages 5: The Impact of the EU’s CBAM on
China’s Carbon Emission Policy
Authors: Ruiyu Geng, Qianyi Cai, Hanbin Wang
First page: 5
Abstract: The European Union’s Carbon Border Adjustment Mechanism (CBAM) has not only accelerated the development of China’s energy policy, but it has also posed new obstacles. This paper aims to explore the influence of CBAM at various phases of its evolution on policy formulation for China’s six main high-emission industries. The policy analysis indicates that the Chinese government, in response to the demands of CBAM, has expanded its emission reduction programs. These now include sustainable energy endeavors, like hydrogen, alongside traditional high-emission industries. Moreover, the Chinese government has implemented 39 initiatives for energy conservation and emission reduction. These initiatives seek to restructure industrial frameworks via legislative modifications, enhance manufacturing methodologies, and establish stringent emission regulations. Nonetheless, there is insufficient adherence to policy implementation in China’s high-emission industries, failing to provide substantial reductions in emissions. In light of the demands from the CBAM carbon taxes and its own emission reduction objectives, the Chinese government has prioritized enhancing enforcement in the six main sectors.
Citation: Climate
PubDate: 2024-12-30
DOI: 10.3390/cli13010005
Issue No: Vol. 13, No. 1 (2024)
- Climate, Vol. 13, Pages 6: Navigating the (Im)mobility–Adaptation
Nexus in the Context of Climate and Environmental Change: A Typological
Discussion
Authors: Chiara Bernasconi
First page: 6
Abstract: Since the 1990s, the academic discourse on climate change, migration, and adaptation has undergone significant shift. Individuals previously characterized as “climate refugees” are now cast as adaptable agents. Against this backdrop, academic explorations of the nexus between mobility and adaptation within the context of climate change have burgeoned, particularly in the latter half of the 2000s. The objective of this paper is to identify linkages between adaptation and different forms of (im)mobility situated on the spectrum of movement that has been conceptualized and discussed in theoretical and empirical material. To accomplish this, I undertake an exhaustive review of the extant literature on the subject of climate change-induced (im)mobility and adaptation. This paper suggests three possible types of relationships between (im)mobility and adaptation in the context of climate and environmental change: adaptation in situ, relocation, and migration as an adaptation strategy. These dimensions have so far been treated separately by scholars.
Citation: Climate
PubDate: 2024-12-31
DOI: 10.3390/cli13010006
Issue No: Vol. 13, No. 1 (2024)
- Climate, Vol. 12, Pages 193: Exploring the Impacts of Lifestyle Changes in
the Global Energy Transition: Insights from a Model-Based Analysis Using
PROMETHEUS
Authors: Panagiotis Fragkos, Eleftheria Zisarou, Andreas Andreou
First page: 193
Abstract: A global clean energy transition is required for achieving ambitious climate goals and ensuring sustainable development. While technological advancements are crucial, they are not sufficient on their own to meet Paris Agreement (PA) climate targets. Integrating lifestyle changes, particularly in sectors such as transport and residential use of energy, into climate policies and energy modeling framework is gaining recognition in energy transition research. This study explores the impact of lifestyle changes on the global energy system and CO2 emissions using the PROMETHEUS model, an advanced energy–economy–environment system model. In this research we present scenarios in which lifestyle changes, such as reduced private car use and increased adoption of public transport and energy-savings behavior in households, are gradually introduced and complement technological and policy measures within the energy transition framework. We explore the impacts of scenarios with different levels of climate policies and lifestyle changes to evaluate the effects of various behavioral shifts on global energy consumption and CO2 emissions. Results show that even under current climate policies, lifestyle changes can reduce global energy demand by 5% by 2030 and 10% by 2050. When combined with ambitious decarbonization policies, the reductions are much more significant, leading to a 35% reduction by 2050 compared to the baseline scenario. Overall, the findings suggest that lifestyle changes, when effectively integrated with climate policy measures, can reduce energy demand and carbon emissions, alleviate the pressure on energy supply, and reduce the cost burden for energy producers and consumers.
Citation: Climate
PubDate: 2024-11-21
DOI: 10.3390/cli12120193
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 194: Accounting for Climate and Inherent Soil
Quality in United Nations (UN) Land Degradation Analysis: A Case Study of
the State of Arizona (USA)
Authors: Elena A. Mikhailova, Hamdi A. Zurqani, Lili Lin, Zhenbang Hao, Christopher J. Post, Mark A. Schlautman, Gregory C. Post, George B. Shepherd
First page: 194
Abstract: Climate change and land degradation (LD) are some of the most critical challenges for humanity. Land degradation (LD) is the focus of the United Nations (UN) Convention to Combat Desertification (UNCCD) and the UN Sustainable Development Goal (SDG 15: Life on Land). Land degradation is composed of inherent and anthropogenic LD, which are both impacted by inherent soil quality (SQ) and climate. Conventional LD analysis does not take into account inherent SQ because it is not the result of land use/land cover change (LULC), which can be tracked using remote sensing platforms. Furthermore, traditional LD analysis does not link anthropogenic LD to climate change through greenhouse gas (GHG) emissions. This study uses one of the indicators for LD for SDG 15 (15.3.1: Proportion of land that is degraded over the total land area) to demonstrate how to account for inherent SQ in anthropogenic LD with corresponding GHG emissions over time using the state of Arizona (AZ) as a case study. The inherent SQ of AZ is skewed towards low SQ soils (Entisols: 29.3%, Aridisols: 49.4%), which, when combined with climate, define the inherent LD status. Currently, 8.6% of land in AZ has experienced anthropogenic LD primarily because of developments (urbanization) (42.8%) and agriculture (32.2%). All six soil orders have experienced varying degrees of anthropogenic LD. All land developments in AZ can be linked to damages from LD, with 4862.6 km2 developed, resulting in midpoint losses of 8.7 × 1010 kg of total soil carbon (TSC) and a midpoint social cost of carbon dioxide emissions (SC-CO2) of $14.7B (where B = billion = 109, USD). Arizona was not land degradation neutral (LDN) based on an increase (+9.6%) in the anthropogenic LD overall and an increase in developments (+29.5%) between 2001 and 2021. Considering ongoing climate change impacts in AZ, this increase in urbanization represents reverse climate change adaptation (RCCA) because of the increased population. The state of AZ has 82.0% of the total state area for nature-based solutions (NBS). However, this area is dominated by soils with inherently low SQ (e.g., Entisols, Aridisols, etc.), which complicates efforts for climate change adaptation.
Citation: Climate
PubDate: 2024-11-21
DOI: 10.3390/cli12120194
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 195: Predictors of Climate Change Activism
Communication in Social Networks
Authors: Carl A. Latkin, Lauren Dayton, Kelsie Parker, Rajiv Rimal
First page: 195
Abstract: It is critical to understand the determinants of climate change activism (CCA) and CCA communications (CCAC). Such information can help organizations that are committed to addressing climate understand and predict who will engage in CCA, identify barriers to CCA, and develop programs to address these barriers to diffuse climate change activism messages and behaviors through social networks and to mobilize action. This study longitudinally investigates psychosocial predictors of CCAC. Study participants were drawn from a randomized clinical trial of US adults (N = 622). Participants completed baseline and follow-up surveys between August to September 2022. Logistic regression models assessed psychosocial factors and implementation intention factors that predicted CCAC at follow-up. The multivariate logistic regression model baseline factors of positive social network norms related to CCAC (aOR: 1.25, 95% CI: 1.10–1.43), comfort encouraging others to engage in CCAC (aOR: 1.74, 95% CI: 1.01–2.88), and following a climate change social media account (aOR: 2.65, 95% CI: 1.74–4.02) were significantly associated with CCAC at follow-up. In a sub-analysis, plans on talking within a week and having in-person conversations versus texting/email were positively associated with CCAC. These findings suggest that strategies to improve comfort talking about CCA and implementation intentions may increase interpersonal CCAC.
Citation: Climate
PubDate: 2024-11-22
DOI: 10.3390/cli12120195
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 196: Analyzing Temperature, Precipitation, and
River Discharge Trends in Afghanistan’s Main River Basins Using
Innovative Trend Analysis, Mann–Kendall, and Sen’s Slope
Methods
Authors: Noor Ahmad Akhundzadah
First page: 196
Abstract: Afghanistan, a nation already challenged by geopolitical and environmental pressure, faces severe climate change impacts, evident through rising temperatures, decreasing precipitation, and reduced river discharge. These changes profoundly affect the country’s water resources, agriculture, ecosystems, and well-being. This study analyzes trends in mean annual temperature, precipitation, and river discharge across all five of Afghanistan’s river basins from 1980 to 2022, utilizing an innovative trend analysis (ITA), the Mann–Kendall (MK) test, and Sen’s slope (SS) estimator. Climate data were derived from the CRU TS.v4 and TerraClimate gridded datasets, while river discharge data were obtained from GloFAS-ERA5 datasets. The results reveal significant climate shifts, including a notable 1.5 °C rise in mean annual temperature, significantly higher than the global average of 1.3 °C, a 1.2 mm decrease in mean annual precipitation, and a −128 m3/s reduction in river discharge across all basins since 1980. Climate change impacts were particularly severe in the western part of the country. These findings underscore the strain on Afghanistan’s vulnerable water resources, with critical implications for agriculture and water management, highlighting the urgent need for adaptive strategies to mitigate climate-induced risks.
Citation: Climate
PubDate: 2024-11-22
DOI: 10.3390/cli12120196
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 197: Combining Multi-Source Satellite Data with a
Microclimate Model to Analyze the Microclimate of an Urban Park
Authors: Yi Pan, Takehiro Morimoto, Toshiaki Ichinose
First page: 197
Abstract: Cities concentrate many people, and studies have shown that resultant urban heat islands can be intense. Urban parks can function as “cool islands” that mitigate heat island effects. This study used the microclimate model ENVI-met 5.1 to assess the cooling effect of Panyu Park in the center of Shanghai, China. The primary objectives were to increase the diversity of data sources and to conduct a microclimate analysis. Two scenarios were examined: the actual park and no park. The results indicated that (1) the integration of satellite technology enhanced the data sources for ENVI-met and thereby increased the efficiency of urban modeling and (2) the simulated results for the park correlated well with the actual data observed at weather stations. The presence of the park resulted in a decrease in the maximum air temperature by 0.1 °C at 1.4 m above ground, a decrease in the wind speed by 1.67 m/s, a maximum increase of 0.2% in relative humidity, and a reduction of 1.94 in the Predicted Mean Vote. The results demonstrated the applicability of multi-source satellite data in microclimate research, saved time on data collection, and provided valuable information for studies undertaken in areas where the collection of field data is challenging and/or historical data are unavailable.
Citation: Climate
PubDate: 2024-11-25
DOI: 10.3390/cli12120197
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 198: Estimating Surface Urban Heat Island Effects
of Abeokuta Within the Context of Its Economic Development Cluster in Ogun
State Nigeria: A Baseline Study Utilising Remote Sensing and Cloud-Based
Computing Technologies
Authors: Oluwafemi Michael Odunsi, Andreas Rienow
First page: 198
Abstract: The demands for growth and prosperity in developing countries have prompted Ogun State to initiate six economic development clusters oriented around its urban areas. However, little attention has been given to the environmental impact of these clusters in relation to temperature change and thermal consequences. Serving as a baseline study for the Abeokuta Cluster, whose implementation is still ongoing, this study analysed the surface urban heat island (SUHI) effects for 2003, 2013, and 2023 to determine whether variations in these effects exist over time. The study utilised satellite imagery from Landsat sensors and the cloud computing power of Google Earth Engine for data collection and analysis. Findings revealed that Abeokuta City experienced varying degrees of high SUHI effects, while the surrounding areas proposed for residential and industrial development in the Abeokuta Cluster showed low SUHI effects. The differences in SUHI effects within Abeokuta City across the years were found to be statistically significant (Fwithin = 3.158, p = 0.044; Fbetween = 5.065, p = 0.025), though this was not the case for the Abeokuta cluster as a whole. This study recommends urban planning strategies and policy interventions to combat SUHI effects in Abeokuta City, along with precautionary measures for the Abeokuta Cluster.
Citation: Climate
PubDate: 2024-11-26
DOI: 10.3390/cli12120198
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 199: CO2 Emission from Caves by Temperature-Driven
Air Circulation—Insights from Samograd Cave, Croatia
Authors: Buzjak, Gabrovšek, Perșoiu, Pennos, Paar, Bočić
First page: 199
Abstract: Opposite to atmospheric CO2 concentrations, which reach a minimum during the vegetation season (e.g., June–August in the Northern Hemisphere), soil CO2 reaches a maximum in the same period due to the root respiration. In karst areas, characterized by high rock porosity, this excess CO2 seeps inside caves, locally increasing pCO2 values above 1%. To better understand the role of karst areas in the carbon cycle, it is essential to understand the mechanisms of CO2 dynamics in such regions. In this study, we present and discuss the spatial and temporal variability of air temperature and CO2 concentrations in Samograd Cave, Croatia, based on three years of monthly spot measurements. The cave consists of a single descending passage, resulting in a characteristic bimodal climate, with stable conditions during summer (i.e., stagnant air inside the cave) and a strong convective cell bringing in cold air during winter. This bimodality is reflected in both CO2 concentrations and air temperatures. In summer, the exchange of air through the cave’s main entrance is negligible, allowing the temperature and CO2 concentration to equilibrate with the surrounding rocks, resulting in high in-cave CO2 concentrations, sourced from enhanced root respiration. During cold periods, CO2 concentrations are low due to frequent intrusions of fresh external air, which effectively flush out CO2 from the cave. Both parameters show distinct spatial variability, highlighting the role of cave morphology in their dynamics. The CO2 concentrations and temperatures have increased over the observation period, in line with external changes. Our results highlight the role of caves in transferring large amounts of CO2 from soil to the atmosphere via caves, a process that could have a large impact on the global atmospheric CO2 budget, and thus, call for a more in-depth study of these mechanisms.
Citation: Climate
PubDate: 2024-11-26
DOI: 10.3390/cli12120199
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 200: Estimation and Validation of Snowmelt Runoff
Using Degree Day Method in Northwestern Himalayas
Authors: Sunita, Vishakha Sood, Sartajvir Singh, Pardeep Kumar Gupta, Hemendra Singh Gusain, Reet Kamal Tiwari, Varun Khajuria, Daljit Singh
First page: 200
Abstract: The rivers of the Himalayas heavily rely on the abundance of snow, which serves as a vital source of water to South Asian countries. However, its impact on the hydrological system of the region is mainly felt during the spring season. The melting of snow and consequent base flow significantly contribute to the incoming streamflow. This article examines the evaluation of the proportionate contribution to the total streamflow of Beas River up to Pandoh Dam through the snow melt. To analyze the snow melt, the snowmelt runoff model (SRM) has been utilized via dividing the study area into seven different elevation zones within a range of 853–6582 m and computing the percentage of snow cover, ranging from 15% to 90% across the basin. To validate the accuracy of the model, several metrics, such as coefficient of determination (R2) and volume difference (VD), are utilized. The R2 reveals that over the span of ten years, the daily discharge simulations exhibited efficiency levels ranging from 0.704 to 0.795, with VD falling within the range of 1.47% to 20.68%. This study has revealed that a significant amount of streamflow originates during the summer and monsoon periods, with snowmelt ranging from 10% to 45%. This research provides crucial understanding of the impact of snowmelt on streamflow, supplying essential knowledge on freshwater supply in the area.
Citation: Climate
PubDate: 2024-11-26
DOI: 10.3390/cli12120200
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 201: Assessing the Role of Climate Transition
Bonds in Advancing Green Transformations in Japan
Authors: Zhiying Zhao, Rong Huang, Yanwu Zhang, Yuki Shiga, Rajib Shaw
First page: 201
Abstract: This study investigates the potential of Climate Transition Bonds as strategic financial instruments in promoting green transformations within Japan’s carbon-intensive sectors. Through a qualitative case study approach, we assess four prominent bond issuances—Japan Government Bonds, MUFG Bonds, TEPCO Bonds, and SMBC Bonds—focusing on their contributions to emissions reduction, renewable energy expansion, and technological innovation. The analysis reveals that these bonds play a pivotal role in enabling Japan to advance its carbon neutrality goals by financing key decarbonization projects. However, significant challenges persist, including the limited scalability of emerging technologies, disparities in economic benefits across sectors, and governance inefficiencies that may hinder optimal outcomes. The findings underscore the necessity of refining collaborative governance frameworks to enhance transparency, stakeholder inclusivity and regulatory oversight in the deployment of these bonds. This paper contributes to the discourse on sustainable finance by elucidating the policy implications of climate transition bonds, proposing avenues for improved governance, and highlighting the structural adjustments required to align these financial mechanisms with Japan’s long-term sustainability objectives.
Citation: Climate
PubDate: 2024-11-27
DOI: 10.3390/cli12120201
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 202: Socio-Demographic Determinants of
Climate-Smart Agriculture Adoption Among Smallholder Crop Producers in
Bushbuckridge, Mpumalanga Province of South Africa
Authors: Variety Nkateko Thabane, Isaac Azikiwe Agholor, Moses Zakhele Sithole, Mishal Trevor Morepje, Nomzamo Sharon Msweli, Lethu Inneth Mgwenya
First page: 202
Abstract: Climate-smart agriculture (CSA) is a transformative approach to farming that aims to meet the demands of increasing food production under the growing pressures of climate change. CSA’s goals are to boost agricultural productivity, enhance resilience to climate impacts, and reduce greenhouse gas emissions. Thus, the study explored farmers’ socio-demographic factors influencing the adoption of CSA in sustainable crop production. The study was carried out in Bushbuckridge, Mpumalanga province of South Africa, with a focus on smallholder crop producers in the area. The study surveyed 300 smallholder farmers and employed simple random sampling, structured questionnaires, and a binary logistic regression model for data analysis. The significant and positive socio-demographic variables relevant to the adoption of climate-smart practices were level of education (p < 0.014), household size (p < 0.007), farm experience (p < 0.053), and farmland fertility (p < 0.047). Therefore, for CSA practices to be adopted by smallholder crop producers, a targeted approach is needed to address this issue. Therefore, support and training are needed to bridge the literacy gap among smallholder crop producers with the overall aim of improving their understanding of climate change and CSA practices that can mitigate the effects of climate change.
Citation: Climate
PubDate: 2024-11-29
DOI: 10.3390/cli12120202
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 203: Rethinking Climate Justice: Insights from
Environmental Sociology
Authors: Md Saidul Islam
First page: 203
Abstract: This paper reexamines climate justice through the framework of environmental sociology, offering fresh perspectives on the intersection of social and ecological systems in the face of escalating global climate crises. It emphasizes that inequality lies at the heart of global climate politics, often obstructing pathways toward achieving a true climate solution. Drawing from established traditions within environmental sociology—such as the new ecological paradigm, the post-growth society, and the environmental justice paradigm—the paper advocates for profound systemic and structural reforms in political and economic systems to tackle entrenched inequalities. By integrating these frameworks, the paper proposes a comprehensive model of climate justice, encompassing material, procedural, compensatory, and transformative dimensions of justice. This holistic approach not only addresses environmental sustainability but also prioritizes social equity, ensuring that marginalized communities are included in the global response to climate change. The paper thus positions this model as a critical component of broader environmental and social transformation.
Citation: Climate
PubDate: 2024-12-02
DOI: 10.3390/cli12120203
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 204: It Is Normal: The Probability Distribution of
Temperature Extremes
Authors: Nir Y. Krakauer
First page: 204
Abstract: The probability of heat extremes is often estimated using the non-stationary generalized extreme value distribution (GEVD) applied to time series of annual maximum temperature. Here, this practice was assessed using a global sample of temperature time series, from reanalysis (both at the grid point and the region scale) as well as station observations. This assessment used forecast negative log-likelihood as the main performance measure, which is particularly sensitive to the most extreme heat waves. It was found that the computationally simpler normal distribution outperforms the GEVD in providing probabilistic year-ahead forecasts of temperature extremes. Given these findings, it is suggested to consider alternatives to the GEVD for assessing the risk of extreme heat.
Citation: Climate
PubDate: 2024-12-02
DOI: 10.3390/cli12120204
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 205: Methodology for Obtaining ETo Data for
Climate Change Studies: Quality Analysis and Calibration of the
Hargreaves–Samani Equation
Authors: Antónia Ferreira, Maria do Rosário Cameira, João Rolim
First page: 205
Abstract: Reference evapotranspiration (ETo) is an important part of the water cycle, essential for climate studies, water resource management, and agricultural planning. However, accurate estimation of ETo is challenging when meteorological data are insufficient or of low quality. Furthermore, in climate change studies where large amounts of data need to be managed, it is important to minimize the complexity of the ETo calculation. This study presents a comprehensive approach that integrates data quality analysis with two calibration methods—annual and cluster-based—to improve ETo estimates based solely on temperature data from a set of weather stations (WS). First, the quality and integrity of meteorological data from several WS were analyzed to reduce uncertainty. Second, the Hargreaves–Samani equation (HS) is site calibrated using two approaches: (a) annual calibration, where the radiation coefficient (kRs) is adjusted using a data set covering the entire year; (b) cluster-based calibration, where independent radiation coefficients are adjusted for clusters of years and months. The methodology was evaluated for the Alentejo region in Southern Portugal, using data from 1996 to 2023. When using the original HS equation with a kRs = 0.17 °C−0.5, ETo was estimated with errors from 14.9% to 22.9% with bias ranging from −9.0% to 8.8%. The annual calibration resulted in kRs values between 0.157 and 0.165 °C−0.5 with estimation errors between 13.3% and 20.6% and bias ranging from −1.5% to 1.0% across the different weather stations. Calibration based on clusters of months and years produced unclear results. Dry season months showed better results using cluster-based calibration, while wet season months performed poorly regardless of the calibration approach. The results highlight the importance of meteorological data quality and site-specific calibration for refining temperature-based ETo estimation methods, and for the region studied, the gains do not justify the increased complexity of the cluster-based approach.
Citation: Climate
PubDate: 2024-12-02
DOI: 10.3390/cli12120205
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 206: A Deeper Understanding of Climate Variability
Improves Mitigation Efforts, Climate Services, Food Security, and
Development Initiatives in Sub-Saharan Africa
Authors: Shamseddin M. Ahmed, Hassan A. Dinnar, Adam E. Ahmed, Azharia A. Elbushra, Khalid G. Biro Turk
First page: 206
Abstract: This research utilized the bagging machine learning algorithm along with the Thornthwaite moisture index (TMI) to enhance the understanding of climate variability and change, with the objective of identifying the most efficient climate service pathways in Sub-Saharan Africa (SSA). Monthly datasets at a 0.5° resolution (1960–2020) were collected and analyzed using R 4.2.2 software and spreadsheets. The results indicate significant changes in climatic conditions in Sudan, with aridity escalation at a rate of 0.37% per year. The bagging algorithm illustrated that actual water use was mainly influenced by rainfall and runoff management, showing an inverse relationship with increasing air temperatures. Consequently, sustainable strategies focusing on runoff and temperature control, such as rainwater harvesting, agroforestry and plant breeding were identified as the most effective climate services to mitigate and adapt to climate variability in SSA. The findings suggest that runoff management (e.g., rainwater harvesting) could potentially offset up to 22% of the adverse impacts of climate variability, while temperature control strategies (e.g., agroforestry) could account for the remaining 78%. Without these interventions, climate variability will continue to pose serious challenges to food security, livelihood generations, and regional stability. The research calls for further in-depth studies on the attributions of climate variability using finer datasets.
Citation: Climate
PubDate: 2024-12-02
DOI: 10.3390/cli12120206
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 207: Leadership and Climate Change Mitigation: A
Systematic Literature Review
Authors: Corey McPherson, Amelia Clarke
First page: 207
Abstract: This systematic literature review (SLR) explores leadership and climate change mitigation in cities. In doing so, it investigates explicit meanings of leadership, enablers of leadership, and leadership similarities and differences across regions. The review utilized three databases on 8 March 2024—Scopus, ProQuest, and Web of Science—curating an initial 496 results, resulting in 30 studies in the final analysis, using a two-reviewer screening process to limit bias and ensure consistency of approach. Inclusion criteria included English-language peer-reviewed articles over a ten-year period. The timeframe used was limited to January 2014 to December 2023 (10 years) to focus on the lead up to and post-implementation of the Paris Agreement. Further, empirical and conceptual studies were included to provide readers of this review with a thorough understanding of leadership work completed since 2014. Exclusion criteria included any studies that focus on adaptation measures and forms of leadership where the focus is on the private business, state, or national level, including leadership and climate change mitigation outside the influence of the local government. The study highlights five distinct meanings of leadership using the Braun and Clarke method of thematic analysis. It found leadership themes related to people (e.g., mayors), policy (e.g., ambitious climate plans), ideas (e.g., new concepts), collective action (e.g., motivating others), and mobilizing power (e.g., through regulations). The enablers of leadership included polycentricity, social capital influences, co-creational and mayor leadership, climate governance, and multi-actor coordination. This review segments the studies based on the findings from the literature, which focus on three continents (North America, Europe, and Asia) with a distinct difference in the meaning and enablers of leadership based on region. The 30 articles shared similarities in content, such as strong mayoral influence, but also had some distinct differences, such as how leadership is enacted based on leveraging market mechanisms, policy, and horizontal and vertical coordination. Finally, research gaps were identified, such as the scant focus on leadership and climate change mitigation in the Global South, to enable future research. Limitations of this study include the utilization of three databases, a focus on only English-language peer-reviewed articles, and a strong climate change mitigation focus.
Citation: Climate
PubDate: 2024-12-03
DOI: 10.3390/cli12120207
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 208: Comparative Trend Analysis of Precipitation
Indices in Several Towns of the Sirba River Catchment (Burkina Faso) from
CHIRPS and TAMSAT Rainfall Estimates
Authors: Giorgio Cannella, Alessandro Pezzoli, Maurizio Tiepolo
First page: 208
Abstract: The increasingly frequent pluvial flood of West African urban settlements indicates the need to investigate the drivers of local rainfall changes. However, meteorological stations are few, unevenly distributed, and work irregularly. Daily satellite rainfall datasets can be used. Nevertheless, these products often need to be more accurate due to sensor errors and limitations in retrieval algorithms. The problem is, therefore, how to characterize rainfall where there is a need for ground-based rainfall records or incomplete series. This study aims to characterize urban rainfall using two satellite datasets. The analysis was carried out in the Sirba river catchment, Burkina Faso, using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the Tropical Applications of Meteorology using SATellite and ground-based data (TAMSAT) datasets. Ten indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) of precipitation were calculated, and their statistical trends were evaluated from 1983 to 2023. The study introduces two key innovations: a comparative analysis of precipitation trends using two satellite datasets and applying this analysis to towns within a previously understudied 39,138 km2 catchment area that is frequently flooded. Both datasets agree on the increase of (i) annual cumulative rainfall over all towns, (ii) five-day maximum rainfall over the town of Manni, (iii) rainfall due to very wet days in Gayéri, (iv) days of heavy rainfall in Bogandé, Manni and Yalgho, and (v) days of very heavy rainfall in Yalgho. These findings suggest the need for targeted pluvial flood prevention measures in towns with increasing trends in heavy rainfall.
Citation: Climate
PubDate: 2024-12-04
DOI: 10.3390/cli12120208
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 209: A Novel Index for Agricultural Drought
Measurement: Soil Moisture and Evapotranspiration Revealed Drought Index
(SERDI)
Authors: Hushiar Hamarash, Azad Rasul, Rahel Hamad
First page: 209
Abstract: Droughts are common across various climates, typically caused by prolonged decreases in rainfall. Several factors contribute to drought, including the temperature, wind speed, and relative humidity and the timing, amount, and intensity of rainfall during the growing season. This study introduces the Soil Moisture and Evapotranspiration Revealed Drought Index (SERDI), a new index that combines soil moisture and evapotranspiration (calculated using the Penman–Monteith method) to enhance drought early warning systems. To validate the SERDI, we compared it with other established indices such as the Land Surface Temperature (LST), Vegetation Health Index (VHI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI), using metrics like the R-squared (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), and p-value to assess the accuracy, data variability, and forecast conditions. The results showed a low RMSE and high R2 between the SERDI and the LST and VHI, indicating a strong correlation. However, weaker correlations were observed between the SERDI and NDVI/NDWI, as shown by the lower R2 and higher RMSE values in semi-arid areas. Regions across Iran, Iraq, Syria, Jordan, and Israel experienced mostly moderate to severe drought conditions, with a few areas in Iran and Syria showing normal conditions. The SERDI’s strong correlation with the LST and moderate correlation with the VHI can be attributed to the direct influence of the soil moisture and evapotranspiration on the surface temperature and vegetation health. On the other hand, the weaker correlation with the NDVI and NDWI is due to variability in the vegetation response, irrigation practices, and regional differences. This study concludes that the SERDI is an effective tool for the detection of drought based on soil moisture and evapotranspiration.
Citation: Climate
PubDate: 2024-12-05
DOI: 10.3390/cli12120209
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 210: Climatology and Long-Term Trends in
Population Exposure to Urban Heat Stress Considering Variable Demographic
and Thermo–Physiological Attributes
Authors: Christos Giannaros, Elissavet Galanaki, Ilias Agathangelidis
First page: 210
Abstract: Previous studies assessing population exposure to heat stress have focused primarily on environmental heat loads without accounting for variations in human thermo–physiological responses to heat. A novel 30-year (1991–2020) human thermal bioclimate dataset, consisting of hourly mPET (modified physiologically equivalent temperature) values for diverse populations, was employed in the present study to assist in addressing this gap. Focusing on the Athens urban area (AUA), Greece, the climatology and long-term trends in acclimatization-based strong heat stress (accliSHS) experienced by average male and female adult and senior individuals during the warm period of the year (April–October) were investigated. Results showed that an average adult (senior) in AUA experienced, on average, approximately 13 (18) additional days with at least 1 h accliSHS in 2020 compared with 1991. The increasing rates per year were particularly pronounced for days with ≥6 h accliSHS, indicating a rise in the daily duration of heat stress in AUA from 1991 to 2020. Combining the variations in climate and demographics in AUA during the examined 30-year period, the long-term trends in ≥1 h accliSHS exposure for the study population types were further examined. This analysis revealed that seniors’ exposure to ≥1 h accliSHS in AUA increased by up to +153,000 person-days × year−1 from 1991 to 2020. Increasing population aging was the main driver of this outcome, highlighting the urgent need for heat–health action planning in Greece.
Citation: Climate
PubDate: 2024-12-05
DOI: 10.3390/cli12120210
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 211: Projected Changes in Dry and Wet Spells over
West Africa during Monsoon Season Using Markov Chain Approach
Authors: Jules Basse, Moctar Camara, Ibrahima Diba, Arona Diedhiou
First page: 211
Abstract: This study examines projected changes in dry and wet spell probabilities in West Africa during the July–August–September monsoon season using a Markov chain approach. Four simulations of regional climate models from the CORDEX-Africa program were used to analyze projected changes in intraseasonal variability. The results show an increase in the probability of having a dry day, a dry day preceding a wet day, and a dry day preceding a dry day, and a decrease in the probability of wet days in the Sahel region under anthropogenic forcing scenarios RCP4.5 and RCP8.5. The decrease in wet days is stronger in the far future and under the RCP8.5 scenario (up to −30%). The study also finds that the probability of consecutive dry days (lasting at least 7 days and 10 days) is expected to increase in western Sahel, central Sahel, and the Sudanian Area under both scenarios, with stronger increases in the RCP8.5 scenario. In contrast, a decrease is expected over the Guinea Coast, with the changes being more important under the RCP4.5. Dry spell probabilities increasing in the Sahel areas and in the northern Sudanian Area is linked to the increase in the very wet days (R95P) in the daily rainfall intensity index (SDII). These changes in dry and wet spell probabilities are important for water management decisions and risk reduction in the energy and agricultural sectors. This study also highlights the need for decision-makers to implement mitigation and adaptation policies to minimize the adverse effects of climate change.
Citation: Climate
PubDate: 2024-12-06
DOI: 10.3390/cli12120211
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 212: Decoding Carbon Footprints: How U.S. Climate
Zones Shape Building Emissions
Authors: Ali Nouri, Ming Hu
First page: 212
Abstract: The construction industry accounts for over 40% of carbon emissions in the United States, with embodied carbon—emissions associated with building materials and construction processes—remaining underexplored, particularly regarding the impact of location and climate. This study addresses this gap by investigating the influence of different climate zones on the embodied carbon emissions of residential buildings. Using Building Information Modeling (BIM), 3D models were developed based on the 2021 International Energy Conservation Code (IECC) and International Residential Code (IRC). A lifecycle assessment (LCA) was conducted using Environmental Product Declarations (EPDs) to evaluate the embodied carbon of building materials during the product stage. The findings reveal that buildings in colder climates exhibit higher embodied carbon emissions, ranging from 25,768 kgCO2e in Zone 1 to 40,129 kgCO2e in Zone 8, due to increased insulation requirements. Exterior walls and roofs were identified as significant contributors, comprising up to 34% of total emissions. Sensitivity analysis further indicates that the window-to-wall ratio and interior wall design substantially affect embodied carbon, with baseline emissions around 170 kgCO2e/m2 in warm areas and 255 kgCO2e/m2 in cold areas. These results establish a baseline for lifecycle embodied carbon values across different climate zones in the United States and align with international standards. This study provides valuable insights for policymakers and designers, offering data to inform effective carbon reduction strategies and optimize building designs for sustainability.
Citation: Climate
PubDate: 2024-12-06
DOI: 10.3390/cli12120212
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 213: The Role of Psychological Capital on Climate
Change Adaptation Among Smallholder Farmers in the uMkhanyakude District
of KwaZulu-Natal, South Africa
Authors: Mbongeni Maziya, Lelethu Mdoda, Lungile Pearl Sindiswa Mvelase
First page: 213
Abstract: Climate change and variability pose a challenge to the livelihoods of smallholder farmers. Previous studies on climate change in the context of smallholder farming have mainly focused on the influence of socio-economic factors in understanding farmers’ responses to climate change. However, little is known about the effect of psychological capital on climate change adaptation. There are calls for better empirical models and transdisciplinary approaches to understand the underlying drivers of climate change adaptation in smallholder farming systems. This study draws from behavioural decision research to assess psychological factors influencing climate change adaptation in the uMkhanyakude district of KwaZulu-Natal. This study adopted the Theory of Planned Behaviour to understand the effect of psychological capital on climate change adaptation. Data were collected from a sample of 400 smallholder farmers who were randomly selected from the uMkhanyakude district. Survey data were analysed using a multivariate probit regression model. The results of the multivariate probit regression model indicated that psychological capital (attitudes towards climate change, subjective norms, and trust) played an important role in influencing climate change adaptation. Climate change adaptation is also influenced by the gender of the farmer, education level, household size, and Tropical Livestock Units. These findings underscore the role of psychological capital in shaping climate change adaptation. This study recommends using transdisciplinary approaches (i.e., combining economics and psychology) in evaluating farmers’ responses to climate change.
Citation: Climate
PubDate: 2024-12-08
DOI: 10.3390/cli12120213
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 214: Resilience of Chinese Ports to Tropical
Cyclones: Operational Efficiency and Strategic Importance
Authors: Mark Ching-Pong Poo, Wen Zhang, Leila Kamalian, Tianni Wang, Yui-yip Lau, Tina Ziting Xu
First page: 214
Abstract: This study evaluated the resilience of five major Chinese ports—Shanghai, Tsingtao, Shenzhen, Xiamen, and Qinzhou—against the impacts of tropical cyclones. These ports, as integral global maritime supply chain nodes, face rising vulnerabilities from climate-related disruptions such as typhoons, sea-level rise, and extreme temperature fluctuations. Employing a resilience assessment framework, this study integrated climate and operational data to gauge how cyclone-induced events affect port performance, infrastructure, and economic stability. Multi-centrality analysis and the Borda count method were applied to assess each port’s strategic importance and operational efficiency under cyclone exposure. The findings highlight variations in resilience across the ports, with Shanghai and Tsingtao showing heightened risk due to their critical roles within international logistics networks. This study suggests strategies like strengthening infrastructure, improving emergency responses, and adopting climate-resilient policies to make China’s ports more sustainable and resilient to climate threats. This research offers actionable insights for policymakers and port authorities, contributing to a more climate-resilient maritime logistics framework.
Citation: Climate
PubDate: 2024-12-09
DOI: 10.3390/cli12120214
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 215: Evolution of Bioclimatic Belts in Spain and
the Balearic Islands (1953–2022)
Authors: Christian Lorente, David Corell, María José Estrela, Juan Javier Miró, David Orgambides-García
First page: 215
Abstract: This study examines the spatio-temporal evolution of bioclimatic belts in peninsular Spain and the Balearic Islands from 1953 to 2022 using the World Bioclimatic Classification System and data from 3668 meteorological stations. Findings indicate a shift toward warmer and more arid conditions, with thermotypes showing an increase in mesomediterranean and thermomediterranean types and a decrease in mesotemperate and supratemperate types. Ombrotype analysis revealed a rise in semiarid types and a decline in humid and hyperhumid types. Significant changes occurred in climate transition zones and mountainous regions, where a process of “Mediterraneanisation”—a process characterised by the expansion of warmer and drier conditions typical of Mediterranean climates into previously temperate areas and/or an altitudinal rise in thermotypes—has been observed. The spatial variability of changes in ombrotypes was greater than that in thermotypes, with regions showing opposite trends to the general one. These results highlight the need for adaptive conservation strategies, particularly in mountainous and climate transition areas, where endemic species may face increased vulnerability due to habitat loss and fragmentation. The results of this study provide insight into how climate change is affecting bioclimatological conditions in the Iberian Peninsula and the Balearic Islands.
Citation: Climate
PubDate: 2024-12-10
DOI: 10.3390/cli12120215
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 216: Views of Health Professionals About Climate
and Health in Sierra Leone: A Cross-Sectional Study
Authors: Isaac S. Sesay, Konstantinos C. Makris
First page: 216
Abstract: Climate change presents one of the biggest global threats to society, while the impact of its manifestations on human health has been poorly characterized and quantified, especially in middle- and low-income countries. The perceptual views of health professionals about the climate and health nexus are critical for the effective implementation of climate policies. The Sierra Leone health professionals are no exception to this, and no such data exist for their country. To this extent, we distributed a cross-sectional survey to understand the perceptual views and beliefs of health professionals in Sierra Leone about the climate and health nexus. A validated international questionnaire on the topic was electronically administered to 265 participants. A descriptive analysis of the survey responses was conducted. Results showed that almost all of the respondents (97%) felt that climate change is an important issue; more than half (68%) of them were very worried about climate change, and 28% were somewhat worried. About half of respondents believed that human activities mostly caused climate change, while 40% of health professionals felt this was equally caused by human activities and natural changes in the environment. The need to engage health professionals with the public and policymakers to bring the health effects of climate change to their attention was particularly highlighted; however, most respondents (81%) stated that numerous barriers impede them from doing so. The most widely reported barriers and needs were the need for training to communicate effectively about climate change and health (96%) and guidance on creating sustainable workplaces (94%), followed by the need for lifelong training and education programs on climate and health, and the lack of time (73%). These survey findings would be valuable to policymakers in Sierra Leone and the broader African regions towards mitigating and adapting to climate change threats to human health.
Citation: Climate
PubDate: 2024-12-10
DOI: 10.3390/cli12120216
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 217: Factors Contributing to Effective Climate
Change Adaptation Projects in Water Management: Implications from the
Developing Countries
Authors: Yuki Shiga, Rajib Shaw
First page: 217
Abstract: The adaptation finance gap is widening as the impact of climate change grows more disruptive around the globe. Although progress in adaptation planning and implementation has been observed across all sectors and regions, this trend of a widening resource gap calls for more ‘effective’ climate adaptation projects. Therefore, the purpose of this paper is to provide a comprehensive analysis to explore potential factors contributing to the effectiveness of climate change projects in developing countries with a particular focus on water management financed under multilateral funds that have been implemented on the ground, completed and documented. Thirty-five projects from the multilateral funds were collected and analyzed for this purpose. Project evaluation documents have been studied, and the effectiveness rating at completion has been assessed against possible contributing factors through regression analysis. The results showed that the factors contributing to project effectiveness converge around several elements: (i) capacity building and education ( r > 0.3); (ii) healthy and resilient livelihoods ( r > 0.2); and (iii) climate data and a robust theory of change (stated by >30% of projects). The implications from this study can provide a useful quantitative ground for discussion around the effective adaptation projects in water management as well as inform relevant international processes such as the Global Goal on Adaptation and global stocktake.
Citation: Climate
PubDate: 2024-12-10
DOI: 10.3390/cli12120217
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 218: Multi-Secular Trend of Drought Indices in
Padua, Italy
Authors: Francesca Becherini, Claudio Stefanini, Antonio della Valle, Francesco Rech, Fabio Zecchini, Dario Camuffo
First page: 218
Abstract: The aim of this work is to investigate drought variability in Padua, northern Italy, over a nearly 300-year period, from 1725 to 2023. Two well-established and widely used indices are calculated, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). They are compatible with a data series starting in the early instrumental period, as both can be estimated using only temperature and precipitation data. The Padua daily precipitation and temperature series from the early 18th century, which were recovered and homogenized with current observations, are used as datasets. The standard approach to estimate SPI and SPEI based on gamma and log-logistic probability distribution functions, respectively, is questioned, assessing the fitting performance of different distributions applied to monthly precipitation data. The best-performing distributions are identified for each index and accumulation period at annual and monthly scales, and their normality is evaluated. In general, they detect more extreme drought events than the standard functions. Moreover, the main statistical values of SPI are very similar, regardless of the approach type, as opposed to SPEI. The difference between SPI and SPEI time series calculated with the best-fit approach has increased since the mid-20th century, in particular in spring and summer, and can be related to ongoing global warming, which SPEI takes into account. The innovative trend analysis applied to SPEI12 indicates a general increasing trend in droughts, while for SPI12, it is significant only for severe events. Summer and fall are the most affected seasons. The critical drought intensity–duration–frequency curves provide an easily understandable relationship between the intensity, duration and frequency of the most severe droughts and allow for the calculation of return periods for the critical events of a certain duration. Moreover, the longest and most severe droughts over the 1725–2023 period are identified.
Citation: Climate
PubDate: 2024-12-10
DOI: 10.3390/cli12120218
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 219: Analysis of the Observed Trends in Rainfall
and Temperature Patterns in North-Eastern Nigeria
Authors: Deborah Ishaku, Emmanuel Tanko Umaru, Abel Aderemi Adebayo, Löwner Löwner, Appollonia Aimiosino Okhimamhe
First page: 219
Abstract: The present study offers a comprehensive evaluation of the monthly rainfall and temperature patterns across nine stations and fifty-nine points in North-Eastern Nigeria using NASA’s Prediction of Worldwide Energy Resources data, spanning four decades (1981–2021). By employing the Mann–Kendall (MK) test and inverse distance weighting (IDW) interpolation, the researchers effectively detected and visualized trends in climate variables. The MK test results indicate contrasting rainfall trends, with notable decreases in Akko, Billiri, Maiduguri, Numan, and Yola, and increases in Gombe, Abadam, Biu, and Mubi. The trends in the maximum temperature were found to be statistically significant across all stations, showing a consistent increase, whereas the minimum temperature trends exhibited a slight but insignificant decrease. The application of the Theil–Sen slope estimator quantified these trends, providing nuanced insights into the magnitudes of changes in climate variables. The IDW results further corroborate the general trend of decreasing rainfall (z = −0.442), modest increases in the maximum temperature (z = 0.046), and a marginal decline in the minimum temperature (z = −0.005). This study makes an important contribution by advocating for the proactive dissemination of climate information. Given the evident climate shifts, particularly the increasing temperatures and fluctuating rainfall patterns, timely access to such information is crucial to enhancing climate resilience in the region. The rigorous statistical methods applied and the detailed spatial analysis strengthen the validity of these findings, making this study a valuable resource for both researchers and policymakers aiming to address climate variability in North-Eastern Nigeria. These research results may also be useful for understanding the climate variabilities in different parts of the world.
Citation: Climate
PubDate: 2024-12-11
DOI: 10.3390/cli12120219
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 220: A Comprehensive AI Approach for Monitoring
and Forecasting Medicanes Development
Authors: Javier Martinez-Amaya, Veronica Nieves, Jordi Muñoz-Mari
First page: 220
Abstract: Medicanes are rare cyclones in the Mediterranean Sea, with intensifying trends partly attributed to climate change. Despite progress, challenges persist in understanding and predicting these storms due to limited historical tracking data and their infrequent occurrence, which make monitoring and forecasting difficult. In response to this issue, we present an AI-based system for tracking and forecasting Medicanes, employing machine learning techniques to identify cyclone positions and key evolving spatio-temporal structural features of the cloud system that are associated with their intensification and potential extreme development. While the forecasting model currently operates with limited training data, it can predict extreme Medicane events up to two days in advance, with precision rates ranging from 65% to 80%. These innovative data-driven methods for tracking and forecasting provide a foundation for refining AI models and enhancing our ability to respond effectively to such events.
Citation: Climate
PubDate: 2024-12-13
DOI: 10.3390/cli12120220
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 221: The Drought Regime in Southern Africa:
Long-Term Space-Time Distribution of Main Drought Descriptors
Authors: Fernando Maliti Chivangulula, Malik Amraoui, Mário Gonzalez Pereira
First page: 221
Abstract: Drought consequences depend on its type and class and on the preparedness and resistance of communities, which, in turn, depends on the knowledge and capacity to manage this climate disturbance. Therefore, this study aims to assess the drought regime in Southern Africa based on vegetation and meteorological indices. The SPI and SPEI were calculated at different timescales, using ERA5 data for the 1971–2020 period. The results revealed the following: (i) droughts of various classes at different timescales occurred throughout the study period and region; (ii) a greater Sum of Drought Intensity and Number, in all classes, but lower duration and severity of droughts with the SPI than with the SPEI; (iii) drought frequency varies from 1.3 droughts/decade to 4.5 droughts/decade, for the SPI at 12- to 3-month timescales; (iv) the number, duration, severity and intensity of drought present high spatial variability, which tends to decrease with the increasing timescale; (v) the area affected by drought increased, on average, 6.6%/decade with the SPI and 9.1%/decade with the SPEI; and (vi) a high spatial-temporal agreement between drought and vegetation indices that confirm the dryness of vegetation during drought. These results aim to support policymakers and managers in defining legislation and strategies to manage drought and water resources.
Citation: Climate
PubDate: 2024-12-13
DOI: 10.3390/cli12120221
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 222: “Taking Action in Community Is Much,
Much Preferable to Doing It Alone”: An Examination of Multi-Level
Facilitators of and Barriers to Sustained Collective Climate Change
Activism Among US Residents
Authors: Lauren Dayton, Kelsie Parker, Julia Ross, Saraniya Tharmarajah, Carl Latkin
First page: 222
Abstract: To enact climate mitigation policies, sustained collective activism is essential to create political pressure and prioritize addressing climate change. Climate change activism includes behaviors such as contacting elected officials to urge them to take action on climate change, volunteering, and signing petitions. Climate change activism is often measured as a one-time event, not sustained activism efforts, which are necessary to enact sufficiently impactful policy changes. To examine barriers to and facilitators of sustained climate change activism, 23 in-depth interviews were conducted between August and December 2023 among members of an innovative national climate change-focused organization. Eligibility included being at least 18 years of age, English-speaking, a US resident, and highly engaged in a climate change activism group. Content analysis of interview transcripts was employed, and five themes emerged as barriers, four themes as facilitators, and five themes as both facilitators of and barriers to sustained climate change activism. The study identified strategies to promote the critical behavior of sustained climate change activism, which included fostering a community of climate change activists, clear instructions on how to engage in activism behaviors for all technical abilities, supporting mental health, and creating climate change activism as a habit and identity.
Citation: Climate
PubDate: 2024-12-14
DOI: 10.3390/cli12120222
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 223: Mapping Methane—The Impact of Dairy
Farm Practices on Emissions Through Satellite Data and Machine Learning
Authors: Hanqing Bi, Suresh Neethirajan
First page: 223
Abstract: Methane emissions from dairy farms are a significant driver of climate change, yet their relationship with farm-specific practices remains poorly understood. This study employs Sentinel-5P satellite-derived methane column concentrations as a proxy to examine emission dynamics across 11 dairy farms in Eastern Canada, using data collected between January 2020 and December 2022. By integrating advanced analytics, we identified key drivers of methane concentrations, including herd genetics, feeding practices, and management strategies. Statistical tools such as Variance Inflation Factor (VIF) and Principal Component Analysis (PCA) addressed multicollinearity, stabilizing predictive models. Machine learning approaches—Random Forest and Neural Networks—revealed a strong negative correlation between methane concentrations and the Estimated Breeding Value (EBV) for protein percentage, demonstrating the potential of genetic selection for emissions mitigation. Our approach refined concentration estimates by integrating satellite data with localized atmospheric modeling, enhancing accuracy and spatial resolution. These findings highlight the transformative potential of combining satellite observations, machine learning, and farm-level characteristics to advance sustainable dairy farming. This research underscores the importance of targeted breeding programs and management strategies to optimize environmental and economic outcomes. Future work should expand datasets and apply inversion modeling for finer-scale emission quantification, advancing scalable solutions that balance productivity with ecological sustainability.
Citation: Climate
PubDate: 2024-12-15
DOI: 10.3390/cli12120223
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 224: Development of a Diagnostic Algorithm for
Detecting Freezing Precipitation from ERA5 Dataset: An Adjustment to the
Far East
Authors: Mikhail Pichugin, Irina Gurvich, Anastasiya Baranyuk, Vladimir Kuleshov, Elena Khazanova
First page: 224
Abstract: Freezing precipitation and the resultant ice glaze can have catastrophic impacts on urban infrastructure, the environment, forests, and various industries, including transportation, energy, and agriculture. In this study, we develop and evaluate regional algorithms for detecting freezing precipitations in the Far East, utilizing the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, along with standard meteorological observations for 20 cold seasons (September–May) from 2004 to 2024. We propose modified diagnostic algorithms based on vertical atmospheric temperature and humidity profiles, as well as near-surface characteristics. Additionally, we apply a majority voting ensemble (MVE) technique to integrate outputs from multiple algorithms, thereby enhancing classification accuracy. Evaluation of detection skills shows significant improvements over the original method developed at the Finnish Meteorological Institute and the ERA5 precipitation-type product. The MVE-based method demonstrates optimal verification statistics. Furthermore, the modified algorithms validly reproduce the spatially averaged inter-annual variability of freezing precipitation activity in both continental (mean correlation of 0.93) and island (correlation of 0.54) regions. Overall, our findings offer a more effective and valuable tool for operational activities and climatological assessments in the Far East.
Citation: Climate
PubDate: 2024-12-17
DOI: 10.3390/cli12120224
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 225: Effect of Temperature on the Spread of
Contagious Diseases: Evidence from over 2000 Years of Data
Authors: Mehmet Balcilar, Zinnia Mukherjee, Rangan Gupta, Sonali Das
First page: 225
Abstract: The COVID-19 pandemic led to a surge in interest among scholars and public health professionals in identifying the predictors of health shocks and their transmission in the population. With temperature increases becoming a persistent climate stress, our aim is to evaluate how temperature specifically impacts the incidences of contagious disease. Using annual data from 1 AD to 2021 AD on the incidence of contagious disease and temperature anomalies, we apply both parametric and nonparametric modelling techniques and provide estimates of the contemporaneous, as well as lagged, effects of temperature anomalies on the spread of contagious diseases. A nonhomogeneous hidden Markov model is then applied to estimate the time-varying transition probabilities between hidden states where the transition probabilities are governed by covariates. For all empirical specifications, we find consistent evidence that temperature anomalies have a statistically significant effect on the incidence of a contagious disease in any given year covered in the sample period. The best fit model further indicates that the contemporaneous effect of a temperature anomaly on the response variable is the strongest. As temperature predictions continue to become more accurate, our results indicate that such information can be used to implement effective public health responses to limit the spread of contagious diseases. These findings further have implications for designing cost effective infectious disease control policies for different regions of the world.
Citation: Climate
PubDate: 2024-12-20
DOI: 10.3390/cli12120225
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 226: A Merging Approach for Improving the Quality
of Gridded Precipitation Datasets over Burkina Faso
Authors: Moussa Waongo, Juste Nabassebeguelogo Garba, Ulrich Jacques Diasso, Windmanagda Sawadogo, Wendyam Lazare Sawadogo, Tizane Daho
First page: 226
Abstract: Satellite precipitation estimates are crucial for managing climate-related risks such as droughts and floods. However, these datasets often contain systematic errors due to the observation methods used. The accuracy of these estimates can be enhanced by integrating spatial and temporal resolution data from in situ observations. Nevertheless, the accuracy of the merged dataset is influenced by the density and distribution of rain gauges, which can vary regionally. This paper presents an approach to improve satellite precipitation data (SPD) over Burkina Faso. Two bias correction methods, Empirical Quantile Mapping (EQM) and Time and Space-Variant (TSV), have been applied to the SPD to yield a bias-corrected dataset for the period 1991–2020. The most accurate bias-corrected dataset is then combined with in situ observations using the Regression Kriging (RK) method to produce a merged precipitation dataset. The findings show that both bias correction methods achieve similar reductions in RMS error, with higher correlation coefficients (approximately 0.8–0.9) and a normalized standard deviation closer to 1. However, EQM generally demonstrates more robust and consistent performance, particularly in terms of correlation and RMS error reduction. On a monthly scale, the superiority of EQM is most evident in June, September, and October. Following the merging process, the final dataset, which incorporates satellite information in addition to in situ observations, demonstrates higher performance. It shows improvements in the coefficient of determination by 83%, bias by 11.4%, mean error by 96.7%, and root-mean-square error by 95.5%. The operational implementation of this approach provides substantial support for decision-making in regions heavily reliant on rainfed agriculture and sensitive to climate variability. Delivering more precise and reliable precipitation datasets enables more informed decisions and significantly enhances policy-making processes in the agricultural and water resources sectors of Burkina Faso.
Citation: Climate
PubDate: 2024-12-20
DOI: 10.3390/cli12120226
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 227: The Impact of Climate Change on Energy
Consumption on Small Tropical Islands
Authors: Julien Gargani
First page: 227
Abstract: The anthropic causes of climate change are well known, but the influence of climate change on society needs to be better estimated. This study estimates the impact of climate change on energy consumption on small tropical islands using monthly temperatures and energy production/consumption statistics during the last decades. Here, we show, using energy, meteorological, demographic, and economic datasets, as well as statistical correlations, that energy consumption is sensitive to (i) cyclonic activity and (ii) temperature warming. On small tropical islands, increased electricity consumption correlates with temperatures rising above 26 °C in relation to air conditioner electricity consumption. On La Réunion Island, a +1 °C increase is expected to cause an electricity production of 1.5 MWh/inhabitant per year, representing a growth of 3.2%. Considering that non-renewable sources are primarily used to produce electricity, this feedback contributed significantly (i.e., 2000 to 4000 TWh) to the greenhouse gas increase caused by climate warming over the last decades on tropical islands. Demographic and wealth variations, as well as socio-economic crises, also have a significant impact on energy consumption (2 kWh for 1000 inhabitants, 0.008 GWh/inhabitant growth for a 10,000 GDP/inhabitant growth, and a 0.2 GWh/inhabitant decrease during COVID-19, for annual consumption, respectively) and must be taken into account for decadal variation analysis. The relationship between climate change and energy consumption in tropical areas should be better integrated into climatic scenarios to adapt building isolation and energy production.
Citation: Climate
PubDate: 2024-12-23
DOI: 10.3390/cli12120227
Issue No: Vol. 12, No. 12 (2024)
- Climate, Vol. 12, Pages 186: Economic Impact of Droughts in Southern
Brazil, a Duration Analysis
Authors: Jorge Luis Tonetto, Josep Miquel Pique, Adelar Fochezatto, Carina Rapetti
First page: 186
Abstract: Hydrometeorological hazards are currently a cause for great concern worldwide. Droughts are among the most recurrent events, causing significant losses. This article presents a study on the duration of droughts in the southernmost state of Brazil, which has a large agricultural sector and experiences frequent drought events. The approach focuses on the economic recovery time of municipalities affected by the drought in 2020, 2022 and 2023, using the total value of invoices issued within each municipality between companies and from companies to consumers. The Kaplan–Meier estimator and Cox regression models are applied, incorporating covariates such as the size of the municipality, geographic location, and primary economic activity sector. The results show that the longest recovery period is concentrated in small cities, particularly in those where agriculture or livestock is the primary economic activity. The greatest resilience is observed in cities within the metropolitan region, where economic activity is more concentrated in services and industry and where populations are generally larger. The study identifies that after each drought event, at least 75% of municipalities achieve economic recovery within 3 months. These findings support better planning for both drought prevention and impact reduction and they are relevant for the development of economic and social policies.
Citation: Climate
PubDate: 2024-11-14
DOI: 10.3390/cli12110186
Issue No: Vol. 12, No. 11 (2024)
- Climate, Vol. 12, Pages 187: Rainfall Projections for the Brazilian Legal
Amazon: An Artificial Neural Networks First Approach
Authors: Luiz Augusto Ferreira Monteiro, Francisco Ivam Castro do Nascimento, José Francisco de Oliveira-Júnior, Dorisvalder Dias Nunes, David Mendes, Givanildo de Gois, Fabio de Oliveira Sanches, Cassio Arthur Wollmann, Michel Watanabe, João Paulo Assis Gobo
First page: 187
Abstract: Rainfall in the Brazilian Legal Amazon (BLA) is vital for climate and water resource management. This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. To make an initial approach, Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) were employed to forecast rainfall from 2012 to 2020. The RNN model showed strong alignment with the observed patterns, accurately predicting rainfall seasonality. However, median comparisons revealed fair approximations with discrepancies. The Root Mean Square Error (RMSE) ranged from 6.7 mm to 11.2 mm, and the coefficient of determination (R2) was low in some series. Extensive analyses showed a low Wilmott agreement and high Mean Absolute Percentage Error (MAPE), highlighting limitations in projecting anomalies and days without rain. Despite challenges, this study lays a foundation for future advancements in climate modeling and water resource management in the BLA.
Citation: Climate
PubDate: 2024-11-15
DOI: 10.3390/cli12110187
Issue No: Vol. 12, No. 11 (2024)
- Climate, Vol. 12, Pages 188: Extreme Precipitation Events During the Wet
Season of the South America Monsoon: A Historical Analysis over Three
Major Brazilian Watersheds
Authors: Aline Araújo de Freitas, Vanessa Silveira Barreto Carvalho, Michelle Simões Reboita
First page: 188
Abstract: Most of South America, particularly the region between the southern Amazon and southeastern Brazil, as well as a large part of the La Plata Basin, has its climate regulated by the South American Monsoon System. Extreme weather and climate events in these areas have significant socioeconomic impacts. The Madeira, São Francisco, and Paraná river basins, three major watersheds in Brazil, are especially vulnerable to wet and drought periods due to their importance as freshwater ecosystems and sources of water for consumption, energy generation, and agriculture. The scarcity of surface meteorological stations in these basins makes meteorological studies challenging, often using reanalysis and satellite data. This study aims to identify extreme weather (wet) and climate (wet and drought) events during the extended wet season (October to March) from 1980 to 2022 and evaluate the performance of two gridded datasets (CPC and ERA5) to determine which best captures the observed patterns in the Madeira, São Francisco, and Paraná river basins. Wet weather events were identified using the 95th percentile, and wet and drought periods were identified using the Standardized Precipitation Index (SPI) on a 6-month scale. In general, CPC data showed slightly superior performance compared to ERA5 in reproducing statistical measures. For extreme day precipitation, both datasets captured the time series pattern, but CPC better reproduced extreme values and trends. The results also indicate a decrease in wet periods and an increase in drought events. Both datasets performed well, showing they can be used in the absence of station data.
Citation: Climate
PubDate: 2024-11-15
DOI: 10.3390/cli12110188
Issue No: Vol. 12, No. 11 (2024)
- Climate, Vol. 12, Pages 189: Using Machine Learning for Climate Modelling:
Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as
a Case Study
Authors: Soldatenko, Angudovich
First page: 189
Abstract: This paper explores the capabilities of two types of recurrent neural networks, unidirectional and bidirectional long short-term memory networks, to build a surrogate model for a coupled fast–slow dynamic system and predicting its nonlinear chaotic behaviour. The dynamical system in question, comprising two versions of the classical Lorenz model with a small time-scale separation factor, is treated as an atmosphere–ocean research simulator. In numerical experiments, the number of hidden layers and the number of nodes in each hidden layer varied from 1 to 5 and from 16 to 256, respectively. The basic configuration of the surrogate model, determined experimentally, has three hidden layers, each comprising between 16 and 128 nodes. The findings revealed the advantages of bidirectional neural networks over unidirectional ones in terms of forecasting accuracy. As the forecast horizon increases, the accuracy of forecasts deteriorates, which was quite expected, primarily due to the chaotic behaviour of the fast subsystem. All other things being equal, increasing the number of neurons in hidden layers facilitates the improvement of forecast accuracy. The obtained results indicate that the quality of short-term forecasts with a lead time of up to 0.75 model time units (MTU) improves most significantly. The predictability limit of the fast subsystem (“atmosphere”) is somewhat greater than the Lyapunov time.
Citation: Climate
PubDate: 2024-11-15
DOI: 10.3390/cli12110189
Issue No: Vol. 12, No. 11 (2024)
- Climate, Vol. 12, Pages 190: Application of Machine Learning and
Hydrological Models for Drought Evaluation in Ungauged Basins Using
Satellite-Derived Precipitation Data
Authors: Anjan Parajuli, Ranjan Parajuli, Mandip Banjara, Amrit Bhusal, Dewasis Dahal, Ajay Kalra
First page: 190
Abstract: Drought is a complex environmental hazard to ecosystems and society. Decision-making on drought management options requires evaluating and predicting the extremity of future drought events. In this regard, quantifiable indices such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the standardized streamflow index (SSI) have been commonly used to characterize meteorological and hydrological drought. In general, the estimation and prediction of the indices require an extensive range of precipitation (SPI and SPEI) and discharge (SSI) datasets in space and time domains. However, there is a challenge for long-term and spatially extensive data availability, leading to the insufficiency of data in estimating drought indices. In this regard, this study uses satellite precipitation data to estimate and predict the drought indices. SPI values were calculated from the precipitation data obtained from the Centre for Hydrometeorology and Remote Sensing (CHRS) data portal for a study water basin. This study employs a hydrological model for calculating discharge and drought in the overall basin. It uses random forest (RF) and support vector regression (SVR) as machine learning models for SSI prediction for time scales of 1- and 3-month periods, which are widely used for establishing interactions between predictors and predictands that are both linear and non-linear. This study aims to evaluate drought severity variation in the overall basin using the hydrological model and compare this result with the machine learning model’s results. The results from the prediction model, hydrological model, and the station data show better correlation. The coefficients of determination obtained for 1-month SSI are 0.842 and 0.696, and those for the 3-month SSI are 0.919 and 0.862 in the RF and SVR models, respectively. These results also revealed more precise predictions of machine learning models in the longer duration as compared to the shorter one, with the better prediction result being from the SVR model. The hydrological model-evaluated SSI has 0.885 and 0.826 coefficients of determination for the 1- and 3-month time durations, respectively. The results and discussion in this research will aid planners and decision-makers in managing hydrological droughts in basins.
Citation: Climate
PubDate: 2024-11-17
DOI: 10.3390/cli12110190
Issue No: Vol. 12, No. 11 (2024)
- Climate, Vol. 12, Pages 191: Factors Influencing Rural Women’s
Adoption of Climate Change Adaptation Strategies: Evidence from the Chivi
District of Zimbabwe
Authors: Johanes Belle, Tendai Mapingure, Solomon Temidayo Owolabi
First page: 191
Abstract: The socio-cultural leadership system in rural communities of developing countries is generally gender-biased, thus rendering female-headed households (FHHs) vulnerable to climate change risk. This study explored the factors influencing FHHs’ adoption of a climate change adaptation strategy (CCAS) in Chivi District, Zimbabwe. We used a multistage sampling technique and logistic regression to evaluate 107 women household heads’ livelihood and their decision to adopt the CCAS in Ward 25 of the Chivi District. The results show that the age of the female head significantly influenced the CCAS decision (R2 = −0.073), along with marital status (R2 = 0.110), agricultural training (R2 = 0.133), club membership (R2 = 0.084), and farm size (R2 = 0.014). Access to formal agricultural training plays a prominent role. At the same time, the institutional framework showed variations and laxity on the part of the local government, as access to extension services varies significantly. In addition, education level was reported to have an insignificant (p = 0.098) influence on CCAS adoption. Overall, multiple institutional and socio-economic factors are essential in influencing CCAS decisions. Hence, central and local governments are encouraged to improve outreach strategies on deploying supporting tools, extension agents, and vital stakeholders for strategic information dissemination to sensitize rural dwellers and community leaders on women’s and FHHs’ crucial role in food security and their resilience to climate change risk. Moreover, the educational syllabus can be enhanced at all rural education levels to reshape the norms of future generations against the customary impact of old age on farming approaches and to encourage women’s participation in decision making and interventions, particularly those sensitive to their societal contributions.
Citation: Climate
PubDate: 2024-11-20
DOI: 10.3390/cli12110191
Issue No: Vol. 12, No. 11 (2024)
- Climate, Vol. 12, Pages 192: Global Meta-Analysis of Innovation Attributes
Influencing Climate-Smart Agriculture Adoption for Sustainable Development
Authors: Chin-Ling Lee, Ginger Orton, Peng Lu
First page: 192
Abstract: Climate-smart agricultural technologies offer transformative potential for achieving Sustainable Development Goals, especially in mitigating extreme weather impacts and enhancing food security. Despite this potential, adoption rates remain limited due to various factors, with perceived complexity playing a significant role. This study conducted a systematic review and meta-analysis to assess the influence of perceived innovation complexity on adopting climate-smart technologies. Using frameworks of the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, we systematically reviewed 28 studies and conducted a meta-analysis of 15 studies across diverse geographic contexts. Our findings from the systematic review indicate inconsistent results on the impact of complexity on adoption due to the different items and scales used to measure the concepts of complexity across contexts, suggesting that there is a need for the development of a standardized scale to measure complexity. Results from the meta-analysis generated a summary effect size (r = 0.51, 95% CI = [0.05, 0.72], z = 6.78, p ≤ 0.0001), revealing a significant relationship between perceived complexity and adoption intent. The effect size of 0.51 indicates that higher complexity levels significantly decrease the likelihood of adoption intent for climate-smart technologies. Differences in CSA research trends across geographic regions highlight the need for tailored approaches to technology adoption that take into account the specific capabilities and constraints of each region. These findings provide valuable insights for policymakers, Extension professionals, and technology developers to design interventions to promote ease of use and enhance technology diffusion in sustainable farming practices and food security. These findings contribute to ongoing efforts to foster sustainable agricultural innovations, offering guidance to accelerate the global transition to more resilient farming systems.
Citation: Climate
PubDate: 2024-11-20
DOI: 10.3390/cli12110192
Issue No: Vol. 12, No. 11 (2024)
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