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Climate, Volume 13, Issue 5 (May 2025) – 15 articles

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27 pages, 11799 KiB  
Article
Developing Low-Carbon Pathways for the Transport Sector in Ethiopia
by Obiora A. Nnene, Dereje Senshaw, Mark Zuidgeest, Owen Mwaura and Yizengaw Yitayih
Climate 2025, 13(5), 96; https://doi.org/10.3390/cli13050096 - 6 May 2025
Viewed by 56
Abstract
This paper discusses the development of baseline and mitigation scenarios to guide the creation of a long-term plan supporting low-carbon transport in Ethiopia. Developing this method involved comprehensively reviewing policy documents, collecting historical activity data, and modelling the baseline and mitigation scenarios. The [...] Read more.
This paper discusses the development of baseline and mitigation scenarios to guide the creation of a long-term plan supporting low-carbon transport in Ethiopia. Developing this method involved comprehensively reviewing policy documents, collecting historical activity data, and modelling the baseline and mitigation scenarios. The paper emphasises the importance of stakeholder engagement, which is instrumental in validating the model inputs, policy targets, and results at each stage, ensuring the credibility and robustness of our findings. The scenario development and analysis are based on the IPCC guidelines, informed by the policies of the Government of Ethiopia, and implemented with the Low-Energy Analysis Platform (LEAP). Three net-zero scenarios are assessed for the time horizon between 2020 to 2050. The so-called maximum ambition, NDC-aligned, and late action scenarios reflect the energy requirements and emissions contribution for varying levels of government ambition towards low-carbon interventions in the transport sector. In the baseline scenario, the total amount of carbon emissions is estimated at 4.81 million tonnes of CO2e in 2020, which is projected to increase to over 15 million tonnes by 2050. Under the mitigation scenarios, significant reductions are projected, with specific interventions like electrification in road freight reducing emissions by 9.68 MtCO2e and expanding rail transport reducing 9.95 MtCO2e by 2050 compared to the baseline. Other strategies identified for mitigating transport sector emissions, like improving energy efficiency, encouraging mass transit and non-motorised transport, show good potential for achieving a greener future. With the transport sector in Ethiopia identified as a major contributor to GHG emissions and climate change, this paper underscores the government’s efforts to ensure the long-term sustainability of its transport sector. Full article
(This article belongs to the Special Issue Climate Change and Transport)
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38 pages, 2567 KiB  
Article
Improving Daily CMIP6 Precipitation in Southern Africa Through Bias Correction—Part 1: Spatiotemporal Characteristics
by Amarech Alebie Addisuu, Gizaw Mengistu Tsidu and Lenyeletse Vincent Basupi
Climate 2025, 13(5), 95; https://doi.org/10.3390/cli13050095 - 4 May 2025
Viewed by 235
Abstract
Impact models used in water, ecology, and agriculture require accurate climatic data to simulate observed impacts. Some of these models emphasize the distribution of precipitation within a month or season rather than the overall amount. To meet this requirement, a study applied three [...] Read more.
Impact models used in water, ecology, and agriculture require accurate climatic data to simulate observed impacts. Some of these models emphasize the distribution of precipitation within a month or season rather than the overall amount. To meet this requirement, a study applied three bias correction techniques—scaled distribution mapping (SDM), quantile distribution mapping (QDM), and QDM with a separate treatment for precipitation below and above the 95th percentile threshold (QDM95)—to daily precipitation data from eleven Coupled Model Intercomparison Project Phase 6 (CMIP6) models, using the Climate Hazards Group Infrared Precipitation with Station version 2 (CHIRPS) as a reference. This study evaluated the performance of all bias-corrected CMIP6 models over Southern Africa from 1982 to 2014 in replicating the spatial and temporal patterns of precipitation across the region against three observational datasets, CHIRPS, the Climatic Research Unit (CRU), and the Global Precipitation Climatology Centre (GPCC), using standard statistical metrics. The results indicate that all bias-corrected precipitation generally performs better than native model precipitation in replicating the observed December–February (DJF) mean and seasonal cycle. The probability density function (PDF) of the bias-corrected regional precipitation indicates that bias correction enhances model performance, particularly for precipitation in the range of 3–35 mm/day. However, both corrected and uncorrected models underestimate higher extremes. The pattern correlations of the bias-corrected precipitation with CHIRPS, the GPCC, and the CRU, as compared to the correlations of native precipitation with the three datasets, have improved from 0.76–0.89 to 0.97–0.99, 0.73–0.87 to 0.94–0.97, and 0.74–0.89 to 0.97–0.99, respectively. Additionally, the Taylor skill scores of the models for replicating the CHIRPS, GPCC, and CRU precipitation spatial patterns over Southern Africa have improved from 0.57–0.80 to 0.79–0.95, 0.55–0.76 to 0.80–0.91, and 0.54–0.75 to 0.81–0.91, respectively. Overall, among the three bias correction techniques, QDM consistently demonstrated better performance than both QDM95 and SDM across various metrics. The implementation of distribution-based bias correction resulted in a significant reduction in bias and improved the spatial consistency between models and observations over the region. Full article
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22 pages, 2060 KiB  
Article
Extreme Weather Shocks and Crime: Empirical Evidence from China and Policy Recommendations
by Huaxing Lin and Ping Jiang
Climate 2025, 13(5), 94; https://doi.org/10.3390/cli13050094 - 3 May 2025
Viewed by 165
Abstract
Rising global temperatures and increasing extreme weather events pose challenges to social stability and public security. This study examines the relationship between extreme weather and crime in China using fixed-effects quasi-Poisson and negative binomial regression models, along with a generalized additive model to [...] Read more.
Rising global temperatures and increasing extreme weather events pose challenges to social stability and public security. This study examines the relationship between extreme weather and crime in China using fixed-effects quasi-Poisson and negative binomial regression models, along with a generalized additive model to explore nonlinear effects. The results show that extreme heat significantly increases crime, following an “S” shaped pattern. This intense heat heightens emotional instability and impulsivity, leading to a crime surge. While moderate heat reduces crime, extreme cold and heavy rainfall have no significant effects. These findings highlight the need for stratified policy interventions. Based on empirical evidence, this study proposes three key recommendations: (1) developing a weather warning and public security risk coordination system, (2) promoting community-based crime prevention through mutual assistance networks and infrastructure improvements, and (3) enhancing psychological interventions to mitigate mental health challenges linked to extreme weather. Integrating meteorological data, law enforcement, and interventions to help potential perpetrators can strengthen urban resilience and public safety against climate-induced crime risks. Full article
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30 pages, 2710 KiB  
Article
Improving Daily CMIP6 Precipitation in Southern Africa Through Bias Correction— Part 2: Representation of Extreme Precipitation
by Amarech Alebie Addisuu, Gizaw Mengistu Tsidu and Lenyeletse Vincent Basupi
Climate 2025, 13(5), 93; https://doi.org/10.3390/cli13050093 - 2 May 2025
Viewed by 102
Abstract
Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such events directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study [...] Read more.
Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such events directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study investigates the effectiveness of three bias correction techniques—scaled distribution mapping (SDM), quantile distribution mapping (QDM), and QDM with a focus on precipitation above and below the 95th percentile (QDM95)—and the daily precipitation outputs from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset was served as a reference. The bias-corrected and native models were evaluated against three observational datasets—the CHIRPS, Multi-Source Weighted Ensemble Precipitation (MSWEP), and Global Precipitation Climatology Center (GPCC) datasets—for the period of 1982–2014, focusing on the December-January-February season. The ability of the models to generate eight extreme precipitation indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated. The results show that the native and bias-corrected models captured similar spatial patterns of extreme precipitation, but there were significant changes in the amount of extreme precipitation episodes. While bias correction generally improved the spatial representation of extreme precipitation, its effectiveness varied depending on the reference dataset used, particularly for the maximum one-day precipitation (Rx1day), consecutive wet days (CWD), consecutive dry days (CDD), extremely wet days (R95p), and simple daily intensity index (SDII). In contrast, the total rain days (RR1), heavy precipitation days (R10mm), and extremely heavy precipitation days (R20mm) showed consistent improvement across all observations. All three bias correction techniques enhanced the accuracy of the models across all extreme indices, as demonstrated by higher pattern correlation coefficients, improved Taylor skill scores (TSSs), reduced root mean square errors, and fewer biases. The ranking of models using the comprehensive rating index (CRI) indicates that no single model consistently outperformed the others across all bias-corrected techniques relative to the CHIRPS, GPCC, and MSWEP datasets. Among the three bias correction methods, SDM and QDM95 outperformed QDM for a variety of criteria. Among the bias-corrected strategies, the best-performing models were EC-Earth3-Veg, EC-Earth3, MRI-ESM2, and the multi-model ensemble (MME). These findings demonstrate the efficiency of bias correction in improving the modeling of precipitation extremes in Southern Africa, ultimately boosting climate impact assessments. Full article
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21 pages, 10225 KiB  
Article
Exploring Socio-Spatial Inequalities in Flood Response Using Flood Simulation and Social Media Data: A Case Study of 2020 Flood in Nanjing, China
by Yi Chen, Yang Zhang, Dekai Tao, Wenjie Zhang, Jingxian You, Yuan Li, Yong Lei and Yao Meng
Climate 2025, 13(5), 92; https://doi.org/10.3390/cli13050092 - 30 Apr 2025
Viewed by 139
Abstract
Identifying socio-spatial inequalities in flood resilience is crucial for effective disaster risk management. This study integrates flood susceptibility simulations and Weibo activity data to construct a flood susceptibility index and incorporates socio-spatial differentiation to represent residents’ coping capacities. By combining flood risk awareness [...] Read more.
Identifying socio-spatial inequalities in flood resilience is crucial for effective disaster risk management. This study integrates flood susceptibility simulations and Weibo activity data to construct a flood susceptibility index and incorporates socio-spatial differentiation to represent residents’ coping capacities. By combining flood risk awareness and coping capacity, we develop a comprehensive flood response capability model to examine the spatial patterns of flood resilience inequality. The findings reveal that (1) high flood risk awareness is concentrated near the Yangtze River and major lakes based on social media data and simulations; (2) coping capacity to floods exhibits a central–periphery pattern, with higher resilience in urban centers and gradually decreases gradually to the suburban and exurban areas; (3) communities are classified into four types based on the combination of flood risk awareness and coping capacities. Multiple linear regression analysis indicates that both natural and social factors significantly influence flood response capacity. This research provides critical insights into the spatial patterns of flood resilience, offering valuable guidance for formulating targeted adaptation strategies. Full article
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23 pages, 5566 KiB  
Article
The Impact of Beach Wrack on Greenhouse Gas Emissions from Coastal Soils
by Olga Nesterova, Mariia Bovsun, Andrei Egorin, Andrey Yatsuk, Dmitry Kravchenko, Irina Lisina, Igor Stepochkin and Anastasia Brikmans
Climate 2025, 13(5), 91; https://doi.org/10.3390/cli13050091 - 30 Apr 2025
Viewed by 149
Abstract
The existing management strategies of macrophyte beach wrack are not always environmentally sound. In this study, we tried to assess the impact of the presence or absence of macrophyte beach wrack on the CO2 flux and the possibility of creating an environmentally [...] Read more.
The existing management strategies of macrophyte beach wrack are not always environmentally sound. In this study, we tried to assess the impact of the presence or absence of macrophyte beach wrack on the CO2 flux and the possibility of creating an environmentally sound recycling of macrophyte beach wrack based on their removal from the beach and processing into biochar. The study was conducted on the coast of the Sea of Japan in the bay of Kievka. The Picarro G4301 portable laser gas analyzer was used to measure CO2 fluxes in areas with and without macrophyte beach wrack. The CO2 flux was 23 times higher at plots with macrophyte beach wrack, compared with plots without macrophyte beach wrack. In the plots after manual removal of the macrophyte beach wrack, on average, there was a 1.6-fold decrease in flow values compared to the plots with the macrophyte beach wrack. Considering the frequency of emissions in the study area, which is associated with frequent cyclones and storms, it is possible to organize the systematic cleaning of macrophyte beach wrack for the production of biochar. Creating projects based on the conversion of macrophyte beach wrack into biochar can have both environmental and economic benefits. The environmental benefits include the reduction of CO2 flux at plots after manual removal of macrophyte beach wrack; the long-term storage of carbon from macrophyte beach wrack biomass in the form of biochar; and the reduction of CO2 flux from soils (carbon sequestration) with the correct technology of introducing biochar into the soil. However, for a more accurate assessment, monitoring seasonal measurements and economic calculations of the entire technological chain of production, risks, and footprint are necessary. Full article
(This article belongs to the Special Issue Coastal Hazards under Climate Change)
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27 pages, 800 KiB  
Article
Socio-Economic Determinants of Climate Change Adaptation Strategies Among Smallholder Farmers in Mbombela: A Binary Logistic Regression Analysis
by Thando Dyanty, Isaac Azikiwe Agholor, Tapelo Blessing Nkambule, Andries Agrippa Nkuna, Mzwakhe Nkosi, Shalia Matilda Ndlovu, Jabulani Johannes Mokoena, Pretty Nombulelo Nkosi, Nombuso Precious Nkosi and Thulasizwe Hopewell Makhubu
Climate 2025, 13(5), 90; https://doi.org/10.3390/cli13050090 - 29 Apr 2025
Viewed by 380
Abstract
Climate change poses significant challenges to smallholder farmers, particularly in sub-Saharan Africa, where agriculture is highly vulnerable to changing climatic conditions. This study examines the socioeconomic determinants influencing the adoption of strategies for adapting to climate change among smallholder farmers in Mbombela, South [...] Read more.
Climate change poses significant challenges to smallholder farmers, particularly in sub-Saharan Africa, where agriculture is highly vulnerable to changing climatic conditions. This study examines the socioeconomic determinants influencing the adoption of strategies for adapting to climate change among smallholder farmers in Mbombela, South Africa. A quantitative research approach was employed, using structured questionnaires to collect data from 308 randomly selected smallholder farmers. Furthermore, the study utilised binary logistic regression to analyse the relationship between socioeconomic factors and the adoption of adaptation strategies. The results revealed that gender, age, income sources, access to climate information, and cooperative membership significantly influenced farmers’ adoption of adaptation strategies. Findings further showed that female farmers, older farmers, and those relying solely on farming income were less likely to adopt adaptation strategies, while younger farmers and those with diversified income sources were more likely to embrace adaptation strategies. Moreover, the study found that access to climate information and cooperative membership were negatively associated with the adoption of adaptation strategies. This negative association may be attributed to inefficiencies in current information dissemination, where climate-related information may not be tailored to the specific needs of farmers, or to cooperative structures that may not effectively facilitate knowledge sharing or collective action. The study concludes that targeted interventions, such as gender-sensitive policies, livelihood diversification, improved extension services, and strengthened cooperative structures, are essential to enhance smallholder farmers’ adaptive capacity. Full article
(This article belongs to the Section Climate Adaptation and Mitigation)
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22 pages, 4743 KiB  
Article
Spatiotemporal Analysis of Urban Heat Islands in Kisangani City Using MODIS Imagery: Exploring Interactions with Urban–Rural Gradient, Building Volume Density, and Vegetation Effects
by Julien Bwazani Balandi, Trésor Mbavumoja Selemani, Jean-Pierre Pitchou Meniko To Hulu, Kouagou Raoul Sambieni, Yannick Useni Sikuzani, Jean-François Bastin, Prisca Tshomba Wola, Jacques Elangilangi Molo, Joël Mobunda Tiko, Bill Mahougnon Agassounon and Jan Bogaert
Climate 2025, 13(5), 89; https://doi.org/10.3390/cli13050089 - 29 Apr 2025
Viewed by 263
Abstract
The urban heat island (UHI) effect has emerged in the literature as a major challenge to urban well-being, primarily driven by increasing urbanization. To address this challenge, this study investigates the spatiotemporal pattern of the UHI in the fast-growing city of Kisangani and [...] Read more.
The urban heat island (UHI) effect has emerged in the literature as a major challenge to urban well-being, primarily driven by increasing urbanization. To address this challenge, this study investigates the spatiotemporal pattern of the UHI in the fast-growing city of Kisangani and within its urban–rural gradient from 2000 to 2024 using land surface temperature (LST) data from the MODIS 11A2 V6.1 product. Inferential and descriptive statistics were applied to examine the patterns of UHI and the relationships between the LST, building volume density (BVD), and vegetation density expressed by the Normalized Difference Vegetation Index (NDVI). The results showed that the spatial extent of the moderate UHI gradually increased from 16 km2 to 38 km2, while the high UHI increased from 9 km2 to 19 km2. Furthermore, although high UHI values (0.2 < UHI ≤ 0.3) are observed in urban areas and significant differences in UHI variations are detected across urban, peri-urban, and rural zones, the results indicate that the mean UHI in Kisangani’s urban areas remains below 0.2. Therefore, based on average UHI variations, Kisangani’s urban zones exhibit moderate disparities in LST compared to rural areas. Moreover, the LST variations significantly correlate with the building volume and vegetation densities. However, the influence of vegetation density as a predictor of LST gradually decreases while the influence of building volume density increases over time, suggesting the need to implement a synergistic development pathway to manage the interactions between urbanization, landscape change, and ecosystem service provision. This integrated approach may represent a crucial solution for mitigating the UHI effect in regions categorized as high-temperature zones. Full article
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36 pages, 375 KiB  
Review
Barriers, Opportunities, and Best Practices for Corporate Climate Transition Plans: A Literature Review
by Daniel Kouloukoui, Nathalie de Marcellis-Warin and Thierry Warin
Climate 2025, 13(5), 88; https://doi.org/10.3390/cli13050088 - 29 Apr 2025
Viewed by 239
Abstract
Corporate climate transition is one of the greatest challenges and opportunities of the 21st century, shaping the future of business sustainability and aligning economic growth with global environmental goals. This article aims to identify the main barriers, opportunities, and best practices associated with [...] Read more.
Corporate climate transition is one of the greatest challenges and opportunities of the 21st century, shaping the future of business sustainability and aligning economic growth with global environmental goals. This article aims to identify the main barriers, opportunities, and best practices associated with the implementation of corporate climate transition plans. Based on a review of studies from leading databases—Scopus, Web of Science, ScienceDirect, and Google Scholar—the research categorizes barriers into economic, financial, political, regulatory, cultural, organizational, and technological dimensions. Opportunities are grouped into areas like sustainable finance, technological innovation, and resilience building. Best practices are organized into clusters, notably governance, energy efficiency, social equity, and just transition frameworks. In addition to advancing academic understanding, this study offers practical implications for key stakeholders. Financial institutions can use these findings to develop climate-aligned financial products tailored to corporate realities. Policymakers can improve regulatory frameworks to foster sustainable business practices and remove legislative barriers. Companies are empowered to refine their climate strategies, address operational constraints, and explore new sustainability-driven opportunities. By integrating scientific insights with real-world applicability, this review contributes to a more holistic understanding of corporate climate transition, bridging academic research with actionable pathways for businesses, financial actors, and public decision-makers. Full article
(This article belongs to the Section Climate Adaptation and Mitigation)
24 pages, 4408 KiB  
Article
Impacts of Urban Morphology on Micrometeorological Parameters and Cyclonic Phenomena in Northern Colombian Caribbean
by Raúl Pérez-Arévalo, Juan E. Jiménez-Caldera, José Luis Serrano-Montes, Jesús Rodrigo-Comino, Juan Carlos Ortiz Royero and Andrés Caballero-Calvo
Climate 2025, 13(5), 87; https://doi.org/10.3390/cli13050087 - 29 Apr 2025
Viewed by 229
Abstract
The rapid urbanization processes across the world can be considered one of the most influential factors in climate change, particularly in metropolitan areas. In South America, the growing population and recurrent non-sustainable or controlled urban land management plans are even increasing the negative [...] Read more.
The rapid urbanization processes across the world can be considered one of the most influential factors in climate change, particularly in metropolitan areas. In South America, the growing population and recurrent non-sustainable or controlled urban land management plans are even increasing the negative consequences of urban heat islands. As a representative case study, Soledad in northern Colombia is an area with recurrent strong wind events, which have caused significant damage to property and human lives, conditioning urban plans. This research aimed to assess the micrometeorological conditions in areas of Soledad, where cyclonic events are highly frequent, to gather essential data on urban planning to understand microclimate changes. We conducted in situ measurements of air temperature, surface temperature, wind speed, relative humidity, and atmospheric pressure across different Local Climate Zones (LCZs). Data were analyzed to assess the impact of urban form, vegetation, and sky openness on microclimatic variations. Our results demonstrated that urban morphology, vegetation cover, and sky openness significantly influenced local microclimates, with lower Sky View Factor (SVF) and higher Leaf Area Index (LAI) values contributing to reduced temperatures and improved airflow. Areas with denser urban canyons exhibited higher temperatures and lower wind speeds, emphasizing the need for strategic urban planning to mitigate heat stress and enhance ventilation. Full article
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15 pages, 3465 KiB  
Article
Wind and Humidity Nexus over Uganda in the Context of Past and Future Climate Volatility
by Ronald Ssembajwe, Amina Twah, Rhoda Nakabugo, Sharif Katende, Catherine Mulinde, Saul D. Ddumba, Yazidhi Bamutaze and Mihai Voda
Climate 2025, 13(5), 86; https://doi.org/10.3390/cli13050086 - 29 Apr 2025
Viewed by 191
Abstract
Wind and humidity are two very vital climate variables that have received little attention by researchers regarding Uganda. This study sought to close this knowledge gap by exposing the dynamics and relationship of windspeed and humidity in Uganda from 1980 to 2023 as [...] Read more.
Wind and humidity are two very vital climate variables that have received little attention by researchers regarding Uganda. This study sought to close this knowledge gap by exposing the dynamics and relationship of windspeed and humidity in Uganda from 1980 to 2023 as well as predicting the future trends from 2025 to 2040. Using high-resolution gridded windspeed and relative humidity (RH) data for the past and seven downscaled and bias-adjusted global climate models within the coupled model intercomparison project phase 6 framework under two shared socioeconomic pathways (SSPs), SPP245 and SSP585, we employed variability, trend, and correlational analyses to expose the wind–humidity nexus at a monthly scale. The results showed a domination of winds of the calm to gentle breeze category across the country, with a maximum magnitude of 6 knots centered over eastern Lake Victoria and eastern Uganda over the historical period. RH was characterized by high to very high magnitudes, except the northern tips of the country, where RH was low for the historical period. Seasonally, both windspeed and RH demonstrated modest variations, with June–July–August (JJA) and September–October–November (SON) having the highest magnitudes, respectively. Similarly, both variables are forecasted to have significant distribution and magnitude changes. For example, windspeeds will be dominated by decreasing trends, while RH will be dominated by increasing trends. Finally, the correlation analysis revealed a strong negative correlation between windspeeds and RH for both the past and future periods, except for the March–April–May (MAM) and September–October–November (SON) seasons, where positive correlations were observed. These findings have practical applications in agriculture, hydrology, thermal comfort, disaster management, and forecasting, especially in the northern, eastern, and Lake Victoria basin regions. The study recommends further finer-scale research at various atmospheric levels and for prolonged future periods and scenarios. Full article
(This article belongs to the Section Climate Dynamics and Modelling)
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36 pages, 5902 KiB  
Review
Arctic Warming: Cascading Climate Impacts and Global Consequences
by Ishfaq Hussain Malik, Rayees Ahmed, James D. Ford and Abdur Rahim Hamidi
Climate 2025, 13(5), 85; https://doi.org/10.3390/cli13050085 - 27 Apr 2025
Viewed by 418
Abstract
The Arctic is undergoing unprecedented transformations with implications for regional ecosystems, Indigenous communities, and global climate systems. Ocean heat transport, permafrost thawing, and ice–albedo interactions are some of the feedback mechanisms that contribute to the increase in average temperatures in the Arctic. These [...] Read more.
The Arctic is undergoing unprecedented transformations with implications for regional ecosystems, Indigenous communities, and global climate systems. Ocean heat transport, permafrost thawing, and ice–albedo interactions are some of the feedback mechanisms that contribute to the increase in average temperatures in the Arctic. These processes increase the risks associated with climate change globally by speeding up the loss of sea ice, changes in biodiversity, and greenhouse gas emissions. This review synthesises recent advances in Arctic climate science, focusing on the drivers and feedback mechanisms of Arctic amplification, its cascading impacts on ecosystems and socioeconomic systems, and emerging governance challenges. It highlights critical knowledge gaps, specifically regarding the importance of Indigenous knowledge and interdisciplinary approaches in climate adaptation strategies. This study emphasises the need for inclusive, transformative, and collaborative approaches by analysing governance frameworks, climate policies, and community resilience initiatives. Innovative adaptation strategies are suggested, such as ecosystem-based adaptations, climate-resilient infrastructure, and the switch to renewable energy to address these issues. Arctic-specific governance recommendations are proposed to develop sustainable solutions that preserve its ecology while reducing its global effects by filling research gaps and promoting international collaboration. The future of the Arctic is not merely a regional issue but also a global one, requiring swift and coordinated action to address climate challenges. Full article
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11 pages, 16684 KiB  
Article
Tropical Sea Surface Temperature and Sea Level as Candidate Predictors for Long-Range Weather and Climate Forecasting in Mid-to-High Latitudes
by Genrikh Alekseev, Sergei Soldatenko, Natalia Glok, Natalia Kharlanenkova, Yaromir Angudovich and Maksim Smirnov
Climate 2025, 13(5), 84; https://doi.org/10.3390/cli13050084 - 27 Apr 2025
Viewed by 149
Abstract
Sea surface temperature (SST) is considered a strong indicator of climate change, being an essential parameter for long-range weather and climate forecasting. Another important indicator of climate change is sea level (SL), which has a longer history of systematic instrumental observations. This paper [...] Read more.
Sea surface temperature (SST) is considered a strong indicator of climate change, being an essential parameter for long-range weather and climate forecasting. Another important indicator of climate change is sea level (SL), which has a longer history of systematic instrumental observations. This paper aims to examine the relationships between low-latitude variations in ocean characteristics (SST and SL) and surface air temperature (SAT) anomalies in the Arctic and mid-latitudes, and discuss the possibility of using SST and SL as predictors to forecast seasonal SAT anomalies. Archives of meteorological observations, atmospheric and oceanic reanalyses, and long-term series of tide gauge data on SL were used in this study. An analysis of relationships between seasonal SAT in different mid-to-high latitude regions and SST made it possible to identify areas in the ocean that have the greatest influence on SAT patterns. The most commonly identified area is located in the tropical North Atlantic. Another area was found in the Indo-Pacific warm pool. The predictive potential of the relationships identified between ocean characteristics (SST and SL) and SAT will be used to build deep learning models aimed at predicting climate variability in mid-to-high latitudes. Full article
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21 pages, 16156 KiB  
Article
Beneficial Analysis of the Effect of Precipitation Enhancement on Highland Barley Production on the Tibetan Plateau Under Different Climate Conditions
by Jiandong Liu, Fei Wang, De Li Liu, Jun Du, Rihan Wu, Han Ding, Fengbin Sun and Qiang Yu
Climate 2025, 13(5), 83; https://doi.org/10.3390/cli13050083 - 26 Apr 2025
Viewed by 153
Abstract
While highland barley on the Tibetan Plateau is adversely affected by water stress during its growth period, precipitation enhancement could potentially mitigate this issue. Accurate assessment of the benefits obtained through precipitation enhancement is crucial for local governments to develop policies for sustainable [...] Read more.
While highland barley on the Tibetan Plateau is adversely affected by water stress during its growth period, precipitation enhancement could potentially mitigate this issue. Accurate assessment of the benefits obtained through precipitation enhancement is crucial for local governments to develop policies for sustainable agriculture. To quantify these benefits, the WOFOST model was employed to evaluate the effects under four different precipitation enhancement scenarios. The model demonstrated strong performance, with a Nash–Sutcliffe Efficiency (NSE) of 0.93 and a root mean square error (RMSE) of 3.66. Using the calibrated WOFOST model, yield increases were simulated under three meteorological drought conditions classified by the Standardized Precipitation Evapotranspiration Index (SPEI). The results showed that yield increases were minimal during years with less rainfall, primarily due to a lower leaf area index under extreme meteorological drought conditions. Additionally, the impact of precipitation enhancement on yield increases was nonlinear. An enhancement of 5% had negligible effects, while enhancements greater than 10% led to significant increases. Specifically, precipitation enhancement during the reproductive stage resulted in regional yield increases of 170.7, 325.5, 465.9, and 580.5 kg/ha for enhancements of 5%, 10%, 15%, and 20%, respectively, surpassing yield increases from enhancements during the vegetative stage. This greater yield increase is attributed to highland barley’s sensitivity to water stress at critical growth stages and the unique climate conditions of the Tibetan Plateau. For Longzi—the largest base for highland barley production, with a planting area of 3440 ha in 2024—a 10% enhancement at the reproductive stage could yield an economic benefit of CNY 9.8 million. Under climate change scenarios, the decreasing trends in highland barley yields could be effectively offset by precipitation enhancement, highlighting the applicability of precipitation enhancement as an effective tool for mitigating climate change in Tibet. Future studies should integrate crop models with weather numerical models to better address uncertainties. Full article
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22 pages, 11091 KiB  
Article
Assessing Climate Change Impacts on Combined Sewer Overflows: A Modelling Perspective
by Panagiota Galiatsatou, Iraklis Nikoletos, Dimitrios Malamataris, Antigoni Zafirakou, Philippos Jacob Ganoulis, Argyro Gkatzioura, Maria Kapouniari and Anastasia Katsoulea
Climate 2025, 13(5), 82; https://doi.org/10.3390/cli13050082 - 22 Apr 2025
Viewed by 281
Abstract
The study examines the impacts of climate change on the operation and capacity of the combined sewer network in the historic center of Thessaloniki, Greece. Rainfall data from three high-resolution Regional Climate Models (RCMs), namely (a) the Cosmo climate model (CCLM), (b) the [...] Read more.
The study examines the impacts of climate change on the operation and capacity of the combined sewer network in the historic center of Thessaloniki, Greece. Rainfall data from three high-resolution Regional Climate Models (RCMs), namely (a) the Cosmo climate model (CCLM), (b) the regional atmospheric climate model (RACMO) and (c) the regional model (REMO), from the MED-CORDEX initiative with future estimations based on Representative Concentration Pathway (RCP) 4.5, are first corrected for bias based on existing measurements in the study area. Intensity–duration–frequency (IDF) curves are then constructed for future data using a temporal downscaling approach based on the scaling of the Generalized Extreme Value (GEV) distribution to derive the relationships between daily and sub-daily precipitation. Projected rainfall events associated with various return periods are subsequently developed and utilized as input parameters for the hydrologic–hydraulic model. The simulation results for each return period are compared with those of the current climate, and the projections from various RCMs are ranked according to their impact on the combined sewer network and overflow volumes. In the short term (2020–2060), the CCLM and REMO project a decrease in CSO volumes compared to current conditions, while the RACMO predicts an increase, highlighting uncertainties in short-term climate projections. In the long term (2060–2100), all models indicate a rise in combined sewer overflow volumes, with CCLM showing the most significant increase, suggesting escalating pressure on urban drainage systems due to more intense rainfall events. Based on these findings, it is essential to adopt mitigation strategies, such as nature-based solutions, to reduce peak flows within the network and alleviate the risk of flooding. Full article
(This article belongs to the Section Climate Dynamics and Modelling)
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