Journal Description
Climate
Climate
is a scientific, peer-reviewed, open access journal of climate science published online monthly by MDPI. The American Society of Adaptation Professionals (ASAP) is affiliated with Climate and its members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), GeoRef, AGRIS, and other databases.
- Journal Rank: CiteScore - Q2 (Atmospheric Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.7 days after submission; acceptance to publication is undertaken in 3.4 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.7 (2022);
5-Year Impact Factor:
3.6 (2022)
Latest Articles
Assessment of the Vulnerability of Households Led by Men and Women to the Impacts of Climate-Related Natural Disasters in the Coastal Areas of Myanmar and Vietnam
Climate 2024, 12(6), 82; https://doi.org/10.3390/cli12060082 (registering DOI) - 2 Jun 2024
Abstract
Farm households along the coastlines of Myanmar and Vietnam are becoming increasingly vulnerable to flooding, saltwater intrusion, and rising sea levels. There is little information available on the relative vulnerability of men- and women-headed households, and the governments of Myanmar and Vietnam have
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Farm households along the coastlines of Myanmar and Vietnam are becoming increasingly vulnerable to flooding, saltwater intrusion, and rising sea levels. There is little information available on the relative vulnerability of men- and women-headed households, and the governments of Myanmar and Vietnam have not identified or implemented any adaptive measures aimed specifically at vulnerable peoples. This study aims to fill these gaps and assess the relative climate change vulnerability of men- and women-headed farm households. This study considers 599 farm households from two regions of Myanmar and 300 households from Thua Thien Hue province of Vietnam for the period 2021–2022. We offer a livelihood vulnerability index (LVI) analysis of men- and women-headed farm households using 46 indicators arranged into seven major components. The aggregate LVI scores indicate that farm households in Myanmar are more vulnerable (scores of 0.459 for men and 0.476 for women) to climate-related natural disasters than farm households in Vietnam (scores of 0.288 for men and 0.292 for women), regardless of the gender of the head of household. Total vulnerability indexing scores indicate that women-headed households are more vulnerable than men-headed households in both countries. Poor adaptive capacity and highly sensitive LVI dimensional scores explain the greater vulnerability of women-headed farm households. The findings also highlight the importance of the adaptive capacity components reflected in the LVI analysis in reducing farm households’ vulnerability.
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(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)
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Beyond the First Tipping Points of Southern Hemisphere Climate
by
Terence J. O’Kane, Jorgen S. Frederiksen, Carsten S. Frederiksen and Illia Horenko
Climate 2024, 12(6), 81; https://doi.org/10.3390/cli12060081 (registering DOI) - 31 May 2024
Abstract
Analysis of observations, reanalysis, and model simulations, including those using machine learning methods specifically designed for regime identification, has revealed changes in aspects of the Southern Hemisphere (SH) circulation and Australian climate and extremes over the last half-century that indicate transitions to new
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Analysis of observations, reanalysis, and model simulations, including those using machine learning methods specifically designed for regime identification, has revealed changes in aspects of the Southern Hemisphere (SH) circulation and Australian climate and extremes over the last half-century that indicate transitions to new states. In particular, our analysis shows a dramatic shift in the metastability of the SH climate that occurred in the late 1970s, associated with a large-scale regime transition in the SH atmospheric circulation, with systematic changes in the subtropical jet, blocking, zonal winds, and storm tracks. Analysis via nonstationary clustering reveals a regime shift coincident with a sharp transition to warmer oceanic sea surface temperatures and increased baroclinicity in the large scales of the Antarctic Circumpolar Circulation (ACC), extending across the whole hemisphere. At the same time, the background state of the tropical Pacific thermocline shoaled, leading to an increased likelihood of El Niño events. The SH climate shift in the late 1970s is the first hemispheric regime shift that can be directly attributed to anthropogenic climate change. These changes in dynamics are associated with additional regional tipping points, including reductions in mean and extreme rainfall in south-west Western Australia (SWWA) and streamflow into Perth dams, and also with increases in mean and extreme rainfall over northern Australia since the late 1970s. The drying of south-eastern Australia (SEA) occurred against a background of accelerating increases in average and extreme temperatures across the whole continent since the 1990s, implying further inflection points may have occurred. Analysis of climate model simulations capturing the essence of these observed shifts indicates that these systematic changes will continue into the late 21st century under high greenhouse gas emission scenarios. Here, we review two decades of work, revealing for the first time that tipping points characteristic of regime transitions are inferred to have already occurred in the SH climate system.
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Open AccessArticle
Assessment of Rural Flood Risk and Factors Influencing Household Flood Risk Perception in the Haut-Bassins Region of Burkina Faso, West Africa
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Madou Sougué, Bruno Merz, Amadé Nacanabo, Gnibga Issoufou Yangouliba, Ibrahima Pouye, Jean Mianikpo Sogbedji and François Zougmoré
Climate 2024, 12(6), 80; https://doi.org/10.3390/cli12060080 (registering DOI) - 31 May 2024
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In the past two decades, several floods have affected people and their properties in Burkina Faso, with unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other
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In the past two decades, several floods have affected people and their properties in Burkina Faso, with unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other flood-prone regions in Burkina Faso. Consequently, there is a data and knowledge gap regarding flood risk in the Haut-Bassins region, which in turn hinders the development of mitigation strategies and risk reduction measures in affected communities. This study demonstrates how data collected at the household level can be used to understand flood risk and its components at the village level in this data-scarce region. Using an indicator-based method, we analyzed both flood risk and flood risk perception at the village level. Moreover, we determined the factors influencing flood risk perception at the household level using an ordered logit model. We found that 12 out of the 14 villages in our sample group had experienced high levels of flood risk. The management of runoff from the nearest urban areas as well as poorly designed civil engineering infrastructures, such as roads, were highlighted by households as significant factors that increased their vulnerability. Additionally, we found that the perceived flood risk consistently exceeds the estimated flood risk, with an insignificant positive correlation between both risk indices. Regression results indicate that flood risk perception is mainly influenced by informational and behavioral factors of households. The findings of this study can provide valuable information to municipal and regional authorities involved in disaster risk management within the study area. Moreover, our/this method is transferable to other data-scarce regions.
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Open AccessArticle
Numerical Modeling of Atmospheric Temperature and Stratospheric Ozone Sensitivity to Sea Surface Temperature Variability
by
Sergei P. Smyshlyaev, Andrew R. Jakovlev and Vener Ya Galin
Climate 2024, 12(6), 79; https://doi.org/10.3390/cli12060079 - 27 May 2024
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The results of numerical experiments with a chemistry–climate model of the lower and middle atmosphere are presented to study the sensitivity of the polar stratosphere of the Northern and Southern Hemispheres to sea surface temperature (SST) variability, both as a result of interannual
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The results of numerical experiments with a chemistry–climate model of the lower and middle atmosphere are presented to study the sensitivity of the polar stratosphere of the Northern and Southern Hemispheres to sea surface temperature (SST) variability, both as a result of interannual variability associated with the Southern Oscillation, and because of long-term increases in SST under global warming. An analysis of the results of model experiments showed that for both scenarios of SST changes, the response of the polar stratosphere for the Northern and Southern Hemispheres is very different. In the Arctic, during the El Niño phase, conditions are created for the polar vortex to become less stable, and in the Antarctic, on the contrary, for it to become more stable, which is expressed in a weakening of the zonal wind in the winter in the Arctic and its increase in the Antarctic, followed by a spring decrease in temperature and concentration of ozone in the Antarctic and their increase in the Arctic. Global warming creates a tendency for the polar vortex to weaken in winter in the Arctic and strengthen it in the Antarctic. As a result, in the Antarctic, the concentration of ozone in the polar stratosphere decreases both in winter (June–August) and, especially, in spring (September–November). Global warming may hinder ozone recovery which is expected as a result of the reduced emissions of ozone-depleting substances. The model results demonstrate the dominant influence of Brewer–Dobson circulation variability on temperature and ozone in the polar stratosphere compared with changes in wave activity, both with changes in SST in the Southern Oscillation and with increases in SST due to global warming.
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Open AccessFeature PaperReview
Applying Machine Learning in Numerical Weather and Climate Modeling Systems
by
Vladimir Krasnopolsky
Climate 2024, 12(6), 78; https://doi.org/10.3390/cli12060078 - 26 May 2024
Abstract
In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The
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In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered as a simple introduction to the problem.
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(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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Precipitation Extremes and Trends over the Uruguay River Basin in Southern South America
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Vanessa Ferreira, Osmar Toledo Bonfim, Rafael Maroneze, Luca Mortarini, Roilan Hernandez Valdes and Felipe Denardin Costa
Climate 2024, 12(6), 77; https://doi.org/10.3390/cli12060077 - 22 May 2024
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This study analyzes the spatial distribution and trends in five extreme daily rainfall indices in the Uruguay River Basin (URB) from 1993 to 2022 using the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset. The main findings reveal a predominantly positive trend
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This study analyzes the spatial distribution and trends in five extreme daily rainfall indices in the Uruguay River Basin (URB) from 1993 to 2022 using the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset. The main findings reveal a predominantly positive trend in heavy precipitation (R95p) and extreme precipitation (R99p) events over the mid URB, while a negative trend is observed in the upper and low URB. Significant trends in the frequency of heavy and extreme rainfall were observed during autumn (MAM), with positive trends across most of the mid and upper URB and negative trends in the low URB. In the upper URB, negative trends in the frequency of extremes were also found during spring (SON) and summer (DJF). Overall, there was a reduction in the number of consecutive wet days (CWD), particularly significant in the upper URB and the northern half of the mid URB. Additionally, the upper URB experienced an overall increase in the duration of consecutive dry days (CDD).
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Open AccessArticle
Reliability and Exploratory Factor Analysis of a Measure of the Psychological Distance from Climate Change
by
Alan E. Stewart
Climate 2024, 12(5), 76; https://doi.org/10.3390/cli12050076 - 18 May 2024
Abstract
Psychological distance from climate change has emerged as an important construct in understanding sustainable behavior and attempts to mitigate and/or adapt to climate change. Yet, few measures exist to assess this construct and little is known about the properties of the existing measures.
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Psychological distance from climate change has emerged as an important construct in understanding sustainable behavior and attempts to mitigate and/or adapt to climate change. Yet, few measures exist to assess this construct and little is known about the properties of the existing measures. In this article, the author conducted two studies of a psychological distance measure developed by Wang and her colleagues. In Study 1, the author assessed the test–retest reliability of the measure over a two-week interval and found the scores to be acceptably stable over time. In Study 2, the author conducted two exploratory factor analyses, using different approaches to the correlation and factor extraction. Similar results were observed for each factor analysis: one factor was related to items that specified greater psychological distance from climate change; a second factor involved items that specified closeness to climate change; and a third involved the geographic/spatial distance from climate change. The author discussed the results and provided recommendations on ways that the measure may be used to research the construct of psychological distance from climate change.
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(This article belongs to the Special Issue Anthropogenic Climate Change: Social Science Perspectives - Volume II)
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Open AccessArticle
The Machine Learning Attribution of Quasi-Decadal Precipitation and Temperature Extremes in Southeastern Australia during the 1971–2022 Period
by
Milton Speer, Joshua Hartigan and Lance Leslie
Climate 2024, 12(5), 75; https://doi.org/10.3390/cli12050075 - 17 May 2024
Abstract
Much of eastern and southeastern Australia (SEAUS) suffered from historic flooding, heat waves, and drought during the quasi-decadal 2010–2022 period, similar to that experienced globally. During the double La Niña of the 2010–2012 period, SEAUS experienced record rainfall totals. Then, severe
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Much of eastern and southeastern Australia (SEAUS) suffered from historic flooding, heat waves, and drought during the quasi-decadal 2010–2022 period, similar to that experienced globally. During the double La Niña of the 2010–2012 period, SEAUS experienced record rainfall totals. Then, severe drought, heat waves, and associated bushfires from 2013 to 2019 affected most of SEAUS, briefly punctuated by record rainfall over parts of inland SEAUS in the late winter/spring of 2016, which was linked to a strong negative Indian Ocean Dipole. Finally, from 2020 to 2022 a rare triple La Niña generated widespread extreme rainfall and flooding in SEAUS, resulting in massive property and environmental damage. To identify the key drivers of the 2010–2022 period’s precipitation and temperature extremes due to accelerated global warming (GW), since the early 1990s, machine learning attribution has been applied to data at eight sites that are representative of SEAUS. Machine learning attribution detection was applied to the 52-year period of 1971–2022 and to the successive 26-year sub-periods of 1971–1996 and 1997–2022. The attributes for the 1997–2022 period, which includes the quasi-decadal period of 2010–2022, revealed key contributors to the extremes of the 2010–2022 period. Finally, some drivers of extreme precipitation and temperature events are linked to significant changes in both global and local tropospheric circulation.
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(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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Adaptation through Climate-Smart Agriculture: Examining the Socioeconomic Factors Influencing the Willingness to Adopt Climate-Smart Agriculture among Smallholder Maize Farmers in the Limpopo Province, South Africa
by
Koketso Cathrine Machete, Mmapatla Precious Senyolo and Lungile Sivuyile Gidi
Climate 2024, 12(5), 74; https://doi.org/10.3390/cli12050074 - 17 May 2024
Abstract
Agriculture contributes to the South African economy, but this sector is highly vulnerable to climate change risks. Smallholder maize farmers are specifically susceptible to climate change impacts. The maize crop plays a crucial role in the country’s food security as is considered a
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Agriculture contributes to the South African economy, but this sector is highly vulnerable to climate change risks. Smallholder maize farmers are specifically susceptible to climate change impacts. The maize crop plays a crucial role in the country’s food security as is considered a staple food and feed. The study aimed at examining the socioeconomic factors influencing smallholder maize farmers’ willingness to adopt climate-smart agriculture in the Limpopo Province, South Africa. It was conducted in three different areas due to their specific agro-ecological zones. A multipurpose research design was used to gather data, and multistage random sampling was used to choose the study areas. Subsequently, 209 purposefully selected farmers were interviewed face-to-face using structured questionnaires and focus discussion groups. Descriptive results revealed that 81%, 67%, and 63% farmers in Ga-Makanye, Gabaza, and Giyani were willing to adopt CSA. Using the double-hurdle model, the t-test was significant at 1%, Prob > chi2 = 0. 0000, indicating a good model. At a 5% confidence level, education, crop diversification, and information about climate-smart agriculture (CSA) positively influenced adoption, while household size and agricultural experience negatively influenced it. It is recommended that the Department of Agriculture, Land Reform, and Rural Development provide CSA workshops and educational programs to farmers to enhance their knowledge and decision-making processes regarding adaptation strategies.
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(This article belongs to the Special Issue Changing Rainfall Patterns and Food Insecurity: Vulnerable Regions and Adaptation Strategies)
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People’s Perception of Climate Change Impacts on Subtropical Climatic Region: A Case Study of Upper Indus, Pakistan
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Bashir Ahmad, Muhammad Umar Nadeem, Saddam Hussain, Abid Hussain, Zeeshan Tahir Virik, Khalid Jamil, Nelufar Raza, Ali Kamran and Salar Saeed Dogar
Climate 2024, 12(5), 73; https://doi.org/10.3390/cli12050073 - 16 May 2024
Abstract
In developing countries like Pakistan, the preservation of the environment, as well as people’s economies, agriculture, and way of life, are believed to be hampered by climate change. Understanding how people perceive climate change and its signs is essential for creating a variety
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In developing countries like Pakistan, the preservation of the environment, as well as people’s economies, agriculture, and way of life, are believed to be hampered by climate change. Understanding how people perceive climate change and its signs is essential for creating a variety of adaptation solutions. In this study, we aim to bridge the gap in current research within this area, which predominantly relies on satellite data, by integrating qualitative assessments of people’s perceptions of climate change, thereby providing valuable ground-based observations of climate variability and its impacts on local communities. Field-based data were collected at different altitudes (upstream (US), midstream (MS), and downstream (DS)) of the Upper Indus Basin using both quantitative and qualitative assessments in 2017. The result shows that these altitudes are highly variable in many contexts: socioeconomic indicators of education, agriculture, income, women empowerment, health, access to basic resources, and livelihood diversifications are highly variable in the Indus Basin. The inhabitants of the Indus Basin perceive the climate changing around them and report impacts of this change as increase in overall temperatures (US 96.9%, MS 97%, DS 93.6%) and erratic rainfall patterns (US 44.1%, MS 73.3%, DS 51.0%) resulting in increased water availability for crops (US 38.6%, MS 39.7%, DS 54.8%) but also increasing number of dry days (US 56.7%, MS 85.5%, DS 67.1%). Communities at these altitudes said that agriculture was their primary source of income, making them particularly vulnerable to the effects of climate change and the dangers that go along with it. The insights are useful for determining what information and actions are required to support local climate-related hazard management in subtropical climate regions. Moreover, it is vital to launch a campaign to raise awareness of potential hazards, as well as to provide training and an early warning system.
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(This article belongs to the Special Issue Anthropogenic Climate Change: Social Science Perspectives - Volume II)
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Open AccessArticle
Lake Kinneret and Hula Valley Ecosystems under Climate Change and Anthropogenic Involvement
by
Moshe Gophen
Climate 2024, 12(5), 72; https://doi.org/10.3390/cli12050072 - 16 May 2024
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The long-term record of ecological, limnological and climatological parameters that were documented in the Kinneret drainage basin was statistically evaluated. The dependent relations between environmental parameters and a change in climate conditions open a consequence dispute between three optional definitions: long-term instability, climate
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The long-term record of ecological, limnological and climatological parameters that were documented in the Kinneret drainage basin was statistically evaluated. The dependent relations between environmental parameters and a change in climate conditions open a consequence dispute between three optional definitions: long-term instability, climate change impact and ecosystem resiliency. The Kinneret drainage basin during the Anthropocene era is marked by intensive anthropogenic involvement: Increase in population size, drainage of the wetlands and old lake Hula, agricultural development, enhancement of lake Kinneret utilization for water supply, hydrological management, fishery and recreation. Therefore, the impact of a combination of natural and anthropogenic environmental factors confounded each other, and the uniqueness of climate change is unclear.
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Open AccessArticle
Quantifying Downstream Climate Impacts of Sea Surface Temperature Patterns in the Eastern Tropical Pacific Using Clustering
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Jason Finley, Boniface Fosu, Chris Fuhrmann, Andrew Mercer and Johna Rudzin
Climate 2024, 12(5), 71; https://doi.org/10.3390/cli12050071 - 16 May 2024
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El Niño–Southern Oscillation (ENSO) phases and flavors, as well as off-equatorial climate modes, strongly influence sea surface temperature (SST) patterns in the eastern tropical Pacific and downstream climate. Prior studies rely on EOFs (which characterize fractional SST variance) to diagnose climate-scale SST structures,
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El Niño–Southern Oscillation (ENSO) phases and flavors, as well as off-equatorial climate modes, strongly influence sea surface temperature (SST) patterns in the eastern tropical Pacific and downstream climate. Prior studies rely on EOFs (which characterize fractional SST variance) to diagnose climate-scale SST structures, limiting the ability to link individual ENSO flavors with downstream phenomena. Hierarchical and k-means clustering methods are used to construct Eastern Pacific patterns from the ERSST dataset spanning 1950 to 2021. Cluster analysis allows for the direct linkage of individual SST years/seasons to ENSO phase, providing insight into ENSO flavors and associated downstream impacts. In this study, four clusters are revealed, each depicting unique SST patterns influenced by ENSO and Pacific Meridional Mode (PMM) phases. A case study demonstrating the utility of the clusters was also carried out using accumulated cyclone energy (ACE) in the Atlantic and Eastern Pacific basins. Results showed that Eastern Pacific (EP) El Niño suppresses Atlantic tropical cyclone (TC) activity, while Central Pacific (CP) La Niña enhances it. Further, EP El Niño, coupled with positive PMM, amplifies ACE. Ultimately, the methods used herein offer a cleaner analysis tool for identifying dominant SSTA patterns and employing those patterns to diagnose downstream climatic effects.
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Open AccessArticle
Classification of Rainfall Intensity and Cloud Type from Dash Cam Images Using Feature Removal by Masking
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Kodai Suemitsu, Satoshi Endo and Shunsuke Sato
Climate 2024, 12(5), 70; https://doi.org/10.3390/cli12050070 - 12 May 2024
Abstract
Weather Report is an initiative from Weathernews Inc. to obtain sky images and current weather conditions from the users of its weather app. This approach can provide supplementary weather information to radar observations and can potentially improve the accuracy of forecasts However, since
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Weather Report is an initiative from Weathernews Inc. to obtain sky images and current weather conditions from the users of its weather app. This approach can provide supplementary weather information to radar observations and can potentially improve the accuracy of forecasts However, since the time and location of the contributed images are limited, gathering data from different sources is also necessary. This study proposes a system that automatically submits weather reports using a dash cam with communication capabilities and image recognition technology. This system aims to provide detailed weather information by classifying rainfall intensities and cloud formations from images captured via dash cams. In models for fine-grained image classification tasks, there are very subtle differences between some classes and only a few samples per class. Therefore, they tend to include irrelevant details, such as the background, during training, leading to bias. One solution is to remove useless features from images by masking them using semantic segmentation, and then train each masked dataset using EfficientNet, evaluating the resulting accuracy. In the classification of rainfall intensity, the model utilizing the features of the entire image achieved up to 92.61% accuracy, which is 2.84% higher compared to the model trained specifically on road features. This outcome suggests the significance of considering information from the whole image to determine rainfall intensity. Furthermore, analysis using the Grad-CAM visualization technique revealed that classifiers trained on masked dash cam images particularly focused on car headlights when classifying the rainfall intensity. For cloud type classification, the model focusing solely on the sky region attained an accuracy of 68.61%, which is 3.16% higher than that of the model trained on the entire image. This indicates that concentrating on the features of clouds and the sky enables more accurate classification and that eliminating irrelevant areas reduces misclassifications.
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(This article belongs to the Special Issue Extreme Weather Detection, Attribution and Adaptation Design)
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Open AccessCommunication
Were the 2022 Summer Heatwaves a Strong Cause of Europe’s Excess Deaths?
by
Jarle Aarstad
Climate 2024, 12(5), 69; https://doi.org/10.3390/cli12050069 - 9 May 2024
Abstract
During the 2022 summer, Europe experienced heatwaves with record temperatures, and a study has argued that they caused about 62,000 deaths between 30 May and 4 September. The total number of excess deaths during the same period was about 137,000, indicating that the
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During the 2022 summer, Europe experienced heatwaves with record temperatures, and a study has argued that they caused about 62,000 deaths between 30 May and 4 September. The total number of excess deaths during the same period was about 137,000, indicating that the heatwaves were a substantial contributor. Not ruling out that explanation entirely, this paper argues that it was unlikely a strong cause. First, if the heatwaves were a strong cause of numerous deaths, one would assume that the older and deprived were relatively likely to die. However, during the 2022 summer heatwaves in England, which were claimed to have caused about 2900 deaths, the oldest age cohort did not have a higher excess death rate than the middle age cohort, and the excess death rate actually decreased with deprivation status. Moreover, Iceland had among Europe’s highest excess death rates during the summer, which cannot be attributed to heatwaves. During June, July, and August 2022, comparable southern hemisphere countries furthermore had high excess death rates, which cannot be attributed to heatwaves either, as it was during their winter. Also, Europe’s excess death rate was higher during the 2022–2023 winter than during the 2022 summer, and intuitively not attributed to heatwaves, but neither to cold weather, as that winter was abnormally mild. Finally, the paper discusses the puzzling issue that about 56% more women than men, relative to the population, presumably died from the heatwaves.
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(This article belongs to the Special Issue Climate Impact on Human Health)
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Open AccessArticle
Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa
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Kejin Wu, Sayar Karmakar, Rangan Gupta and Christian Pierdzioch
Climate 2024, 12(5), 68; https://doi.org/10.3390/cli12050068 - 8 May 2024
Abstract
Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only
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Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers.
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(This article belongs to the Special Issue Modeling and Forecasting of Climate Risks)
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Open AccessReview
Two Decades of Integrated Flood Management: Status, Barriers, and Strategies
by
Neil S. Grigg
Climate 2024, 12(5), 67; https://doi.org/10.3390/cli12050067 - 8 May 2024
Abstract
Losses from flood disasters are increasing globally due to climate-driven forces and human factors such as migration and land use changes. The risks of such floods involve multiple factors and stakeholders, and frameworks for integrated approaches have attracted a global community of experts.
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Losses from flood disasters are increasing globally due to climate-driven forces and human factors such as migration and land use changes. The risks of such floods involve multiple factors and stakeholders, and frameworks for integrated approaches have attracted a global community of experts. The paper reviews the knowledge base for integrated flood risk management frameworks, including more than twenty bibliometric reviews of their elements. The knowledge base illustrates how integrated strategies for the reduction of flood risk are required at different scales and involve responses ranging from climate and weather studies to the construction of infrastructure, as well as collective action for community resilience. The Integrated Flood Management framework of the Associated Programme on Flood Management of the World Meteorological Organization was developed more than twenty years ago and is explained in some detail, including how it fits within the Integrated Water Resources Management concept that is managed by the Global Water Partnership. The paper reviews the alignment of the two approaches and how they can be used in tandem to reduce flood losses. Success of both integrated management approaches depends on governance and institutional capacity as well as technological advances. The knowledge base for flood risk management indicates how technologies are advancing, while more attention must be paid to social and environmental concerns, as well as government measures to increase participation, awareness, and preparedness. Ultimately, integrated flood management will involve solutions tailored for individual situations, and implementation may be slow, such that perseverance and political commitment will be needed.
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(This article belongs to the Special Issue Advances of Flood Risk Assessment and Management)
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Open AccessArticle
Developing a Drought Resilience Matrix to Evaluate Water Supply Alternatives
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Krystal Okpa, Zeinab Farahmandfar and Masoud Negahban-Azar
Climate 2024, 12(5), 66; https://doi.org/10.3390/cli12050066 - 7 May 2024
Abstract
Cities around the world are facing increased sensitivity to drought effects. Climate-change-induced drought affects not only the natural hydrology of the broad macroclimate but also those in the urban microclimates. The increasing frequency and duration of droughts are creating challenges for urban water
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Cities around the world are facing increased sensitivity to drought effects. Climate-change-induced drought affects not only the natural hydrology of the broad macroclimate but also those in the urban microclimates. The increasing frequency and duration of droughts are creating challenges for urban water utilities to convey water through distribution systems to customers reliably and consistently. This has led many urban areas like San Francisco, California, to search for unique alternative water supply projects to help bolster the drought resilience of the coupled human and natural water system. This paper focuses on applying the features of resilience (i.e., plan, adapt, absorb, and recover) through a drought resilience matrix to water supply alternatives to analyze how the addition of these projects would increase the overall water system’s drought resilience. San Francisco, California, was used as the case study to test the use of this matrix. Three portfolios (modifying existing supply, recycling, and desalination, as well as local approaches) were created and tested in the matrix. Each portfolio is composed of various alternative water supply projects that the San Francisco Public Utilities Commission (SFPUC) is considering for implementation. Results concluded that the local approaches portfolio provided the most drought resilience, with the recycling and desalination portfolio providing the least resilience. The study approach and the presented findings will provide guidance to water utility professionals in supply planning to enhance drought resilience.
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(This article belongs to the Special Issue Coping with Flooding and Drought)
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A Survey of African Weather and Climate Extremes
by
Mark R. Jury
Climate 2024, 12(5), 65; https://doi.org/10.3390/cli12050065 - 5 May 2024
Abstract
A survey of African weather and climate extremes in the period 1970–2023 reveals spatial and temporal patterns of intense dry and wet spells, associated with meteorological conditions and consequences. Seasonal wind storms occur along coasts facing the Mozambique Channel, the Gulf of Guinea,
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A survey of African weather and climate extremes in the period 1970–2023 reveals spatial and temporal patterns of intense dry and wet spells, associated with meteorological conditions and consequences. Seasonal wind storms occur along coasts facing the Mozambique Channel, the Gulf of Guinea, the Mediterranean, and the Southern Ocean. Desiccating evaporation is found along the edge of the Sahara and Kalahari Deserts, as well as in lowland subtropical river valleys. The Palmer Drought Severity Index (PDSI) and net outgoing longwave radiation (OLR) reflect precipitation–evaporation balance and guide regional evaluation. Temporal fluctuations are dominated by inter-decadal oscillations and drying/moistening trends over Southeast/West Africa, respectively. Localized floods and droughts are frequent, but widespread impacts are rare, suggesting that the transfer of resources from surplus to deficit regions is possible. Various case studies focus on (i) tropical cyclone impacts, (ii) monsoon moisture flux, and (iii) coastal upwelling. African communities have become resilient in the face of extreme weather and have shown that adaptation is possible, but further mitigating efforts are needed so that macro-economic progress does not come with harmful secondary consequences.
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(This article belongs to the Special Issue Hydroclimate Dynamics and Extreme Weather Events in Africa)
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Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach
by
Fabio Di Nunno, Nazzareno Diodato, Gianni Bellocchi, Carla Tricarico, Giovanni de Marinis and Francesco Granata
Climate 2024, 12(5), 64; https://doi.org/10.3390/cli12050064 - 3 May 2024
Abstract
Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of
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Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of climate change, focusing specifically on reference evapotranspiration (ETo) and its associated hydrological variables. The seasonal Mann–Kendall (MK) test was used to assess trends in gridded data. The K-means algorithm was then applied to divide Lazio into four homogeneous regions (clusters), each characterized by distinct trends in hydrological variables. The analysis revealed statistically significant increasing trends (p ≤ 0.01) in temperature, solar radiation, and ETo, with more marked effects observed in the coastal and hilly clusters. In contrast, statistically significant decreasing trends (p ≤ 0.01) were observed for relative humidity, while no statistically significant trends (p > 0.01) were observed for precipitation. This study’s methodology, combining trend analysis and clustering, provides a comprehensive view of ETo dynamics in Lazio, aiding in pattern recognition and identifying regions with similar trends.
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(This article belongs to the Special Issue Regional Special Issue: Climate Change in Italy)
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Investigating Road Ice Formation Mechanisms Using Road Weather Information System (RWIS) Observations
by
Menglin Jin and Douglas G. McBroom
Climate 2024, 12(5), 63; https://doi.org/10.3390/cli12050063 - 2 May 2024
Abstract
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Ice formation on roads leads to a higher incidence of accidents and increases winter de-icing/anti-icing costs. This study analyzed 3 years (2019–2021) of Road Weather Information System (RWIS) sub-hourly measurements collected by the Montana Department of Transportation (MDT) to understand the first-order factors
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Ice formation on roads leads to a higher incidence of accidents and increases winter de-icing/anti-icing costs. This study analyzed 3 years (2019–2021) of Road Weather Information System (RWIS) sub-hourly measurements collected by the Montana Department of Transportation (MDT) to understand the first-order factors of road ice formation and its mechanisms. First, road ice is formed only when the road pavement surface temperature is equal to or below the freezing point (i.e., 32 °F (i.e., 0 °C)), while the corresponding 2 m air temperature could be above 32 °F. Nevertheless, when the road pavement was below 32 °F ice often did not form on the roads. Therefore, one challenge is to know under what conditions road ice forms. Second, the pavement surface temperature is critical for road ice formation. The clear road (i.e., with no ice or snow) surface pavement temperature is generally warmer than the air temperature during both day and night. This feature is different from a natural land surface, where the land skin temperature is lower than the air temperature on cloud-free nights due to radiative cooling. Third, subsurface temperature, measured using a RWIS subsurface sensor below a road surface, did not vary as much as the pavement temperature and, thus, may not be a good index for road ice formation. Fourth, urban heat island effects lead to black ice formation more frequently than roads located in other regions. Fifth, evaporative cooling from the water surface near a road segment further reduces the outlying air temperature, a mechanism that increases heat loss for bridges or lake-side roads in addition to radiative cooling. Additionally, mechanical lifting via mountains and hills is also an efficient mechanism that makes the air condense and, consequently, form ice on the roads. Forecasting road ice formation is in high demand for road safety. These observed features may help to develop a road ice physical model consisting of functions of hyper-local weather conditions, local domain knowledge, the road texture, and geographical environment.
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