The temporal and spatial variation in seasonal sea ice in Hudson Strait is examined using time series and spatial clustering analyses. For the period from 1971 to 2018, a time series of sea ice breakup and freeze-up dates and ice-free season length at twenty-four grid points were generated from sea ice charts derived from satellite and other data. These data were analyzed temporally and spatially. The temporal analyses indicated an unambiguous response to a warming climate with statistically significant earlier breakup dates, later freeze-up dates, and longer ice-free seasons, that were statistically linked to coincident regional surface air temperatures. The rate of change in freeze-up dates and ice-free season length was particularly strong in the early 2000s and less so in the 2010s. There was evidence that breakup date behaviour was not only coincident with regional temperatures but likely with temperature and ice conditions of the previous year. Later freeze-up dates were directly linked to earlier breakup dates using detrended time series. Spatial clustering analysis on the Hudson Strait gridded sea ice data revealed distinctive signatures for Ungava Bay, Frobisher Bay, and for grid points close to the shore and a clear linkage to the underlying circulation of Hudson Strait.
{"title":"Spatial and Temporal Evolution of Seasonal Sea Ice Extent of Hudson Strait, Canada, 1971–2018","authors":"Sławomir Kowal, William A. Gough, Kenneth Butler","doi":"10.3390/cli12070103","DOIUrl":"https://doi.org/10.3390/cli12070103","url":null,"abstract":"The temporal and spatial variation in seasonal sea ice in Hudson Strait is examined using time series and spatial clustering analyses. For the period from 1971 to 2018, a time series of sea ice breakup and freeze-up dates and ice-free season length at twenty-four grid points were generated from sea ice charts derived from satellite and other data. These data were analyzed temporally and spatially. The temporal analyses indicated an unambiguous response to a warming climate with statistically significant earlier breakup dates, later freeze-up dates, and longer ice-free seasons, that were statistically linked to coincident regional surface air temperatures. The rate of change in freeze-up dates and ice-free season length was particularly strong in the early 2000s and less so in the 2010s. There was evidence that breakup date behaviour was not only coincident with regional temperatures but likely with temperature and ice conditions of the previous year. Later freeze-up dates were directly linked to earlier breakup dates using detrended time series. Spatial clustering analysis on the Hudson Strait gridded sea ice data revealed distinctive signatures for Ungava Bay, Frobisher Bay, and for grid points close to the shore and a clear linkage to the underlying circulation of Hudson Strait.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Magallanes-Quintanar, C. E. Galván-Tejada, J. Galván-Tejada, Hamurabi Gamboa-Rosales, S. J. Méndez-Gallegos, Antonio García-Domínguez
Certain impacts of climate change could potentially be linked to alterations in rainfall patterns, including shifts in rainfall intensity or drought occurrences. Hence, predicting droughts can provide valuable assistance in mitigating the detrimental consequences associated with water scarcity, particularly in agricultural areas or densely populated urban regions. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applied an Auto-Machine-Learning approach to deploy Artificial Neural Network models, aiming to predict the Standardized Precipitation Index in four regions of Zacatecas, Mexico. Climatological time-series data spanning from 1979 to 2020 were utilized as predictive variables. The best models were found using performance metrics that yielded a Mean Squared Error, Mean Absolute Error, and Coefficient of Determination ranging from 0.0296 to 0.0388, 0.1214 to 0.1355, and 0.9342 to 0.9584, respectively, for the regions under study. As a result, the Auto-Machine-Learning approach successfully developed and tested Artificial Neural Network models that exhibited notable predictive capabilities when estimating the monthly Standardized Precipitation Index within the study region.
{"title":"Auto-Machine-Learning Models for Standardized Precipitation Index Prediction in North–Central Mexico","authors":"R. Magallanes-Quintanar, C. E. Galván-Tejada, J. Galván-Tejada, Hamurabi Gamboa-Rosales, S. J. Méndez-Gallegos, Antonio García-Domínguez","doi":"10.3390/cli12070102","DOIUrl":"https://doi.org/10.3390/cli12070102","url":null,"abstract":"Certain impacts of climate change could potentially be linked to alterations in rainfall patterns, including shifts in rainfall intensity or drought occurrences. Hence, predicting droughts can provide valuable assistance in mitigating the detrimental consequences associated with water scarcity, particularly in agricultural areas or densely populated urban regions. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applied an Auto-Machine-Learning approach to deploy Artificial Neural Network models, aiming to predict the Standardized Precipitation Index in four regions of Zacatecas, Mexico. Climatological time-series data spanning from 1979 to 2020 were utilized as predictive variables. The best models were found using performance metrics that yielded a Mean Squared Error, Mean Absolute Error, and Coefficient of Determination ranging from 0.0296 to 0.0388, 0.1214 to 0.1355, and 0.9342 to 0.9584, respectively, for the regions under study. As a result, the Auto-Machine-Learning approach successfully developed and tested Artificial Neural Network models that exhibited notable predictive capabilities when estimating the monthly Standardized Precipitation Index within the study region.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Bulmez, A. Brezeanu, George Dragomir, Ovidiu-Mircea Talabă, Gabriel Năstase
Earth’s climate cannot be ignored any longer. Policies are vital in order to mitigate the negative effects of climate change. The energy crisis created by the Russo-Ukrainian war in Europe and COVID-19 pandemic affected the EU and its member states. The focus is more than ever on its energy policies and independence. The EU revised the energy strategy in response to the regional conflict, and it sped up all the processes for energetic independence from other countries outside of the EU. This benefited the climate change policies the most, as all the measures involved reducing energy consumption and increasing renewables, thus contributing to reducing greenhouse gas emissions. As a member state of the EU, Romania is committed to complying with EU regulations. With a high degree of energy independence compared with the other EU members, Romania plans to become a regional energy provider and modernize the energy infrastructure internally as a response to the regional conflict. The measures that the EU and Romania implemented after the conflict started in 2022 have come to fruition, and the effects are becoming visible a year later. This study aims to study the energy strategy of Romania in correlation with the EU strategy in the turbulent period of pandemics and conflict between 2019 and 2023, with the latest available data.
{"title":"An Analysis of Romania’s Energy Strategy: Perspectives and Developments since 2020","authors":"A. Bulmez, A. Brezeanu, George Dragomir, Ovidiu-Mircea Talabă, Gabriel Năstase","doi":"10.3390/cli12070101","DOIUrl":"https://doi.org/10.3390/cli12070101","url":null,"abstract":"Earth’s climate cannot be ignored any longer. Policies are vital in order to mitigate the negative effects of climate change. The energy crisis created by the Russo-Ukrainian war in Europe and COVID-19 pandemic affected the EU and its member states. The focus is more than ever on its energy policies and independence. The EU revised the energy strategy in response to the regional conflict, and it sped up all the processes for energetic independence from other countries outside of the EU. This benefited the climate change policies the most, as all the measures involved reducing energy consumption and increasing renewables, thus contributing to reducing greenhouse gas emissions. As a member state of the EU, Romania is committed to complying with EU regulations. With a high degree of energy independence compared with the other EU members, Romania plans to become a regional energy provider and modernize the energy infrastructure internally as a response to the regional conflict. The measures that the EU and Romania implemented after the conflict started in 2022 have come to fruition, and the effects are becoming visible a year later. This study aims to study the energy strategy of Romania in correlation with the EU strategy in the turbulent period of pandemics and conflict between 2019 and 2023, with the latest available data.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141663328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over the past few decades, the use of vulnerability assessments has grown substantially to support rural communities in developing countries. These studies aim to help these communities achieve their livelihood goals, such as sustainable resource use and adaptation to global changes, by evaluating their susceptibility to climate change impacts. This systematic review critically examines the extensive body of literature on Livelihood Vulnerability Index (LVI) assessments related to climate change impacts in developing countries. By synthesizing findings from various studies, this review highlights patterns and methodologies used to understand the effects of climate change on vulnerable populations. Key focus areas include geographical distribution, methodological approaches, and the frameworks utilized in vulnerability assessments. The review identifies prominent frameworks, such as the LVI and LVI-IPCC, which integrate indicators of sensitivity, exposure, and adaptive capacity to evaluate climate risks. Findings reveal a concentration of studies in Asia and Africa, with a strong emphasis on agricultural and coastal ecosystems. Methodologically, there is a notable reliance on stratified random sampling to accurately capture community and household-level vulnerabilities. A detailed comparative analysis of the LVI, LVI-IPCC, and Sustainable Livelihood Framework (SLF) is also presented, highlighting their characteristics, benefits, and limitations. The review underscores the need for methodological refinements to better address temporal and regional variations in vulnerability. It concludes with recommendations for future research, integrating broader climate scenarios, exploring sectoral interdependencies, and adopting dynamic approaches to enhance the accuracy and applicability of vulnerability assessments.
{"title":"Taking Stock of Recent Progress in Livelihood Vulnerability Assessments to Climate Change in the Developing World","authors":"A. Zainab, Kalim U. Shah","doi":"10.3390/cli12070100","DOIUrl":"https://doi.org/10.3390/cli12070100","url":null,"abstract":"Over the past few decades, the use of vulnerability assessments has grown substantially to support rural communities in developing countries. These studies aim to help these communities achieve their livelihood goals, such as sustainable resource use and adaptation to global changes, by evaluating their susceptibility to climate change impacts. This systematic review critically examines the extensive body of literature on Livelihood Vulnerability Index (LVI) assessments related to climate change impacts in developing countries. By synthesizing findings from various studies, this review highlights patterns and methodologies used to understand the effects of climate change on vulnerable populations. Key focus areas include geographical distribution, methodological approaches, and the frameworks utilized in vulnerability assessments. The review identifies prominent frameworks, such as the LVI and LVI-IPCC, which integrate indicators of sensitivity, exposure, and adaptive capacity to evaluate climate risks. Findings reveal a concentration of studies in Asia and Africa, with a strong emphasis on agricultural and coastal ecosystems. Methodologically, there is a notable reliance on stratified random sampling to accurately capture community and household-level vulnerabilities. A detailed comparative analysis of the LVI, LVI-IPCC, and Sustainable Livelihood Framework (SLF) is also presented, highlighting their characteristics, benefits, and limitations. The review underscores the need for methodological refinements to better address temporal and regional variations in vulnerability. It concludes with recommendations for future research, integrating broader climate scenarios, exploring sectoral interdependencies, and adopting dynamic approaches to enhance the accuracy and applicability of vulnerability assessments.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aaqib Bhat, S. Gupta, S. Singh, Gowhar Meraj, Pankaj Kumar, S. Kanga, Saurabh Singh, Bhartendu Sajan
This study investigated the historical climate data and future projections under the SSP5-8.5 scenario for Jammu, Kashmir (J&K), and its adjoining regions in India. Agriculture is a critical economic pillar of this region, making it highly vulnerable to climate change. This study focused on temperature and precipitation trends. Statistical analysis and modeling methods, including cloud computing, were employed to predict changes and assess their impact on agricultural productivity and water resources. The results indicated that by 2100, the mean maximum and minimum temperatures are projected to increase by approximately 2.90 °C and 2.86 °C, respectively. Precipitation variability is expected to rise, with a mean increase of 2.64 × 10−6 mm per day. These changes have significant consequences for crop yield, water stress, and ecosystem dynamics. An analysis of Gross Primary Productivity (GPP) as a proxy for agricultural productivity using linear regression revealed a concerning trend. Although the total GPP of the study area remained stable over time, it declined by −570 g yr−1 in 2010, coinciding with a 1 °C temperature rise. Projections based on the expected 3 °C temperature increase by 2100 suggest a total GPP loss of −2500 g yr−1. These findings highlight the urgent need for proactive adaptation measures, including sustainable agricultural practices, improved water management, and enhanced socioeconomic infrastructure, to mitigate the impact of climate change and ensure long-term resilience and food security in the region.
{"title":"Simulating Climatic Patterns and Their Impacts on the Food Security Stability System in Jammu, Kashmir and Adjoining Regions, India","authors":"Aaqib Bhat, S. Gupta, S. Singh, Gowhar Meraj, Pankaj Kumar, S. Kanga, Saurabh Singh, Bhartendu Sajan","doi":"10.3390/cli12070099","DOIUrl":"https://doi.org/10.3390/cli12070099","url":null,"abstract":"This study investigated the historical climate data and future projections under the SSP5-8.5 scenario for Jammu, Kashmir (J&K), and its adjoining regions in India. Agriculture is a critical economic pillar of this region, making it highly vulnerable to climate change. This study focused on temperature and precipitation trends. Statistical analysis and modeling methods, including cloud computing, were employed to predict changes and assess their impact on agricultural productivity and water resources. The results indicated that by 2100, the mean maximum and minimum temperatures are projected to increase by approximately 2.90 °C and 2.86 °C, respectively. Precipitation variability is expected to rise, with a mean increase of 2.64 × 10−6 mm per day. These changes have significant consequences for crop yield, water stress, and ecosystem dynamics. An analysis of Gross Primary Productivity (GPP) as a proxy for agricultural productivity using linear regression revealed a concerning trend. Although the total GPP of the study area remained stable over time, it declined by −570 g yr−1 in 2010, coinciding with a 1 °C temperature rise. Projections based on the expected 3 °C temperature increase by 2100 suggest a total GPP loss of −2500 g yr−1. These findings highlight the urgent need for proactive adaptation measures, including sustainable agricultural practices, improved water management, and enhanced socioeconomic infrastructure, to mitigate the impact of climate change and ensure long-term resilience and food security in the region.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141671156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Isaías Lescher Soto, Alicia Villamizar, B. O. Olivares, María Eugenia Gutiérrez, Gustavo J. Nagy
We investigate Venezuela’s potential “futures” under Shared Socioeconomic Pathways (SSPs) through a systematic literature review, including systematic mapping and thematic analysis of 50 scientific articles. We categorised the SSP scenarios into two generational categories and classified the outcomes into positive, negative, and neutral futures. Under first-generation SSP scenarios, increasing poverty could be reversed, and the country’s economic growth could be stimulated by adopting unambitious climate measures. However, second-generation SSP scenarios paint a more challenging picture. They suggest that Venezuela could face heat waves, droughts, an increase in diseases, loss of biodiversity, and an increase in invasive species and pests during the remainder of the 21st century as a direct consequence of climate change. Venezuela’s geographic and topographic diversity could exacerbate these impacts of climate change. For instance, coastal areas could be at risk of sea-level rise and increased storm surges, while mountainous regions could experience more frequent and intense rainfall, leading to landslides and flash floods. The urgency of conducting additional research on the factors that could influence the severity of climate change’s impact, considering Venezuela’s geographic and topographic diversity, cannot be overstated. We also identified the critical need to explore alternative paths to move away from the current extractive development model. The potential actions in this regard could be instrumental in aligning the country with global adaptation and mitigation commitments.
{"title":"Navigating the Uncertain Terrain: Venezuela’s Future Using the Shared Socioeconomic Pathways Framework—A Systematic Review","authors":"Isaías Lescher Soto, Alicia Villamizar, B. O. Olivares, María Eugenia Gutiérrez, Gustavo J. Nagy","doi":"10.3390/cli12070098","DOIUrl":"https://doi.org/10.3390/cli12070098","url":null,"abstract":"We investigate Venezuela’s potential “futures” under Shared Socioeconomic Pathways (SSPs) through a systematic literature review, including systematic mapping and thematic analysis of 50 scientific articles. We categorised the SSP scenarios into two generational categories and classified the outcomes into positive, negative, and neutral futures. Under first-generation SSP scenarios, increasing poverty could be reversed, and the country’s economic growth could be stimulated by adopting unambitious climate measures. However, second-generation SSP scenarios paint a more challenging picture. They suggest that Venezuela could face heat waves, droughts, an increase in diseases, loss of biodiversity, and an increase in invasive species and pests during the remainder of the 21st century as a direct consequence of climate change. Venezuela’s geographic and topographic diversity could exacerbate these impacts of climate change. For instance, coastal areas could be at risk of sea-level rise and increased storm surges, while mountainous regions could experience more frequent and intense rainfall, leading to landslides and flash floods. The urgency of conducting additional research on the factors that could influence the severity of climate change’s impact, considering Venezuela’s geographic and topographic diversity, cannot be overstated. We also identified the critical need to explore alternative paths to move away from the current extractive development model. The potential actions in this regard could be instrumental in aligning the country with global adaptation and mitigation commitments.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141671622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Radial growth of trees, as reflected by tree ring width, serves as a vital proxy for past climate conditions, offering insights into climate dynamics over centennial and millennial time scales. Traditionally, in the high altitudes and latitudes of the central Scandinavian Mountains, summer temperatures, particularly in July, have significantly influenced the radial growth of Scots pine. This research aims to reassess the climatic determinants of Scots pine radial growth in Jämtland, central Scandinavian Mountains, by incorporating a refined analysis that considers temperature, precipitation, and snow depth, and their correlations with tree growth over time. Using a dynamic moving window heatmap correlation analysis, this study revisits a Scots pine chronology to explore the evolving climatic influences on radial growth. This approach allows for the identification of temporal shifts in growth-limiting factors. We observe a notable transition in the 1970s, marking a shift where water availability, rather than temperature, emerges as a critical limiting factor for radial growth at both the beginning and the end of the growing season. This shift is reflective of the broader global trend of decreasing tree growth response to increasing temperatures in the latter half of the 20th century, underscoring the significant impact of ongoing climate change on forest ecosystems. The results highlight the necessity for adaptive forest management strategies that consider the changing dynamics of climatic influences on tree growth. Furthermore, our study contributes to the broader understanding of forest growth patterns in the face of climate change, with substantial implications for ecological research and forest management.
{"title":"Shifts in Climatic Influences on Radial Growth of Scots Pine in the Central Scandinavian Mountains with an Evident Transition in the 1970s","authors":"Ulrika Gomm, Emilia Bromfält, Selma Kling, Qiong Zhang","doi":"10.3390/cli12070097","DOIUrl":"https://doi.org/10.3390/cli12070097","url":null,"abstract":"Radial growth of trees, as reflected by tree ring width, serves as a vital proxy for past climate conditions, offering insights into climate dynamics over centennial and millennial time scales. Traditionally, in the high altitudes and latitudes of the central Scandinavian Mountains, summer temperatures, particularly in July, have significantly influenced the radial growth of Scots pine. This research aims to reassess the climatic determinants of Scots pine radial growth in Jämtland, central Scandinavian Mountains, by incorporating a refined analysis that considers temperature, precipitation, and snow depth, and their correlations with tree growth over time. Using a dynamic moving window heatmap correlation analysis, this study revisits a Scots pine chronology to explore the evolving climatic influences on radial growth. This approach allows for the identification of temporal shifts in growth-limiting factors. We observe a notable transition in the 1970s, marking a shift where water availability, rather than temperature, emerges as a critical limiting factor for radial growth at both the beginning and the end of the growing season. This shift is reflective of the broader global trend of decreasing tree growth response to increasing temperatures in the latter half of the 20th century, underscoring the significant impact of ongoing climate change on forest ecosystems. The results highlight the necessity for adaptive forest management strategies that consider the changing dynamics of climatic influences on tree growth. Furthermore, our study contributes to the broader understanding of forest growth patterns in the face of climate change, with substantial implications for ecological research and forest management.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coastally trapped southerly wind changes are prominent during southeast Australia’s warm season (spring and summer). These abrupt, often gale force, wind changes are known locally as Southerly Busters (SBs) when their wind speeds reach 15 m/s. They move northwards along the coast, often producing very large temperature drops. SBs exceeding 21 m/s are severe SBs (SSBs). SBs have both positive and negative impacts. They bring relief from oppressively hot days but can cause destructive wind damage, worsen existing bushfires, and endanger aviation and marine activities. This study assesses the impacts of global warming (GW) and associated climate change on SBs and SSBs, using observational data from 1970 to 2022. Statistical analyses determine significant trends in annual frequency counts of SBs and SSBs, particularly during the accelerated GW period from the early–mid-1990s. It was found that the annual combined count of SBs and SSBs had increased, with SSBs dominating from 1970 to 1995, but SB frequencies exceeded SSBs from 1996 to 2023. The ascendency of SB frequencies over SSBs since 1996 is explained by the impact of GW on changes in global and local circulation patterns. Case studies exemplify how these circulation changes have increased annual frequencies of SBs, SSBs, and their combined total.
{"title":"Global Warming Impacts on Southeast Australian Coastally Trapped Southerly Wind Changes","authors":"Lance M. Leslie, Milton Speer, Shuang Wang","doi":"10.3390/cli12070096","DOIUrl":"https://doi.org/10.3390/cli12070096","url":null,"abstract":"Coastally trapped southerly wind changes are prominent during southeast Australia’s warm season (spring and summer). These abrupt, often gale force, wind changes are known locally as Southerly Busters (SBs) when their wind speeds reach 15 m/s. They move northwards along the coast, often producing very large temperature drops. SBs exceeding 21 m/s are severe SBs (SSBs). SBs have both positive and negative impacts. They bring relief from oppressively hot days but can cause destructive wind damage, worsen existing bushfires, and endanger aviation and marine activities. This study assesses the impacts of global warming (GW) and associated climate change on SBs and SSBs, using observational data from 1970 to 2022. Statistical analyses determine significant trends in annual frequency counts of SBs and SSBs, particularly during the accelerated GW period from the early–mid-1990s. It was found that the annual combined count of SBs and SSBs had increased, with SSBs dominating from 1970 to 1995, but SB frequencies exceeded SSBs from 1996 to 2023. The ascendency of SB frequencies over SSBs since 1996 is explained by the impact of GW on changes in global and local circulation patterns. Case studies exemplify how these circulation changes have increased annual frequencies of SBs, SSBs, and their combined total.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141707974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Panagiotis Fragkos, Dirk-Jan van de Ven, Russell Horowitz, Eleftheria Zisarou
As the imperative to address climate change intensifies, understanding the effectiveness of policy interventions becomes paramount. In the context of addressing these urgent challenges and given the inadequacy of current policies to address this issue, this study examines the extent to which Nationally Determined Contributions (NDCs) and Long-Term Targets (LTTs) can contribute to achieving ambitious climate goals. Recognizing the critical need for effective climate action, we employ the advanced modelling tools PROMETHEUS and GCAM to assess the implications of different scenarios–Current Policies (CP), Nationally Determined Contributions (NDC), and combination of NDCs with Long-Term Targets (NDC_LTT)–on the future development of energy system and emission. This study, by employing these well-known models, seeks to provide an improved understanding of the impacts of NDCs on global emission trajectories and whether the integration of NDCs and LTTs can help close the gap towards Paris-compatible pathways. The study analyzes various sectors including buildings, transportation, electricity generation, and industry to provide insights into the limitations of existing policies and the potential of enhanced commitments to drive transformative changes in a global scale. The effectiveness of these policies varies across different sectors, highlighting the challenges that need to be addressed for achieving the required emission reduction targets in the medium- and long-term. Key findings indicate significant shifts in energy consumption, fuel mix, technology adoption, and emission trajectories, particularly under the synergistic action represented by the NDC_LTT scenario.
{"title":"Analysing the Transformative Changes of Nationally Determined Contributions and Long-Term Targets","authors":"Panagiotis Fragkos, Dirk-Jan van de Ven, Russell Horowitz, Eleftheria Zisarou","doi":"10.3390/cli12060087","DOIUrl":"https://doi.org/10.3390/cli12060087","url":null,"abstract":"As the imperative to address climate change intensifies, understanding the effectiveness of policy interventions becomes paramount. In the context of addressing these urgent challenges and given the inadequacy of current policies to address this issue, this study examines the extent to which Nationally Determined Contributions (NDCs) and Long-Term Targets (LTTs) can contribute to achieving ambitious climate goals. Recognizing the critical need for effective climate action, we employ the advanced modelling tools PROMETHEUS and GCAM to assess the implications of different scenarios–Current Policies (CP), Nationally Determined Contributions (NDC), and combination of NDCs with Long-Term Targets (NDC_LTT)–on the future development of energy system and emission. This study, by employing these well-known models, seeks to provide an improved understanding of the impacts of NDCs on global emission trajectories and whether the integration of NDCs and LTTs can help close the gap towards Paris-compatible pathways. The study analyzes various sectors including buildings, transportation, electricity generation, and industry to provide insights into the limitations of existing policies and the potential of enhanced commitments to drive transformative changes in a global scale. The effectiveness of these policies varies across different sectors, highlighting the challenges that need to be addressed for achieving the required emission reduction targets in the medium- and long-term. Key findings indicate significant shifts in energy consumption, fuel mix, technology adoption, and emission trajectories, particularly under the synergistic action represented by the NDC_LTT scenario.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claudio Stefanini, F. Becherini, Antonio della Valle, Dario Camuffo
The Padua temperature series is one of the longest in the world, as daily observations started in 1725 and have continued almost unbroken to the present. Previous works recovered readings from the original logs, and digitalized and corrected observations from errors due to instruments, calibrations, sampling times and exposure. However, the series underwent some changes (location, elevation, observing protocols, and different averaging methods) that affected the homogeneity between sub-series. The aim of this work is to produce a homogenized temperature series for Padua, starting from the results of previous works, and connecting all the periods available. The homogenization of the observations has been carried out with respect to the modern era. A newly released paleo-reanalysis dataset, ModE-RA, is exploited to connect the most ancient data to the recent ones. In particular, the following has been carried out: the 1774–2023 daily mean temperature has been homogenized to the modern data; for the first time, the daily values of 1765–1773 have been merged and homogenized; and the daily observations of the 1725–1764 period have been connected and homogenized to the rest of the series. Snowfall observations, extracted from the same logs from which the temperatures were retrieved, help to verify the robustness of the homogenization procedure by looking at the temperature frequency distribution on snowy days, before and after the correction. The possibility of adding new measurements with no need to apply transformations or homogenization procedures makes it very easy to update the time series and make it immediately available for climate change analysis.
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