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27 pages, 12462 KiB  
Article
Long-Term Teleconnections Between Global Circulation Patterns and Interannual Variability of Surface Air Temperature over Kingdom of Saudi Arabia
by Abdullkarim K. Almaashi, Hosny M. Hasanean and Abdulhaleem H. Labban
Atmosphere 2024, 15(11), 1310; https://doi.org/10.3390/atmos15111310 - 30 Oct 2024
Viewed by 367
Abstract
Surface air temperature (SAT) variability is investigated for advancing our understanding of the climate patterns over the Kingdom of Saudi Arabia (KSA). SAT variability reveals significant warming trends, particularly from 1994 onward, as demonstrated by nonlinear and linear trend analysis. This warming is [...] Read more.
Surface air temperature (SAT) variability is investigated for advancing our understanding of the climate patterns over the Kingdom of Saudi Arabia (KSA). SAT variability reveals significant warming trends, particularly from 1994 onward, as demonstrated by nonlinear and linear trend analysis. This warming is linked to global climate patterns, which serve as significant indicators for studying the effects of climate change on surface air temperature patterns across the KSA. The empirical orthogonal function (EOF) method is employed for analyzing SAT due to its effectiveness in extracting dominant patterns of variability during the winter (DJF) and summer (JJA) seasons. The first mode (EOF1) for both seasons shows positive variability across the KSA, explaining more than 45% of the variance. The second mode (EOF2) indicates negative variability in central and northern regions. The third mode (EOF3) describes positive variability but with lower variance over time. PC1 is used to describe the physical mechanism of SAT variability and correlations with global sea surface temperature (SST). The physical mechanism shows that the variability in Mediterranean troughs during the winter season and high pressure over the Indian Ocean and central Asia controls SAT variability over the KSA. The correlation coefficients (CCs) were calculated during the winter and summer season between the SAT of the KSA and six teleconnection indices, El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Atlantic Meridional Mode (AMM), Pacific Warm Pool (PWP), North Atlantic Oscillation (NAO), and Tropical North Atlantic (TNA) SST for the period from 1994 to 2022. ENSO shifts from positive to negative correlations with SAT from winter to summer. IOD shows a diminished correlation with SAT due to the absence of upper air dynamics. PWP consistently enhances surface warming in both seasons through upper air convergence during both seasons. AMM and NAO have a non-significant impact on SAT; however, TNA contributes warming over central and northern parts during winter and summer seasons. The seasonal SAT variations emphasize the significant role of ENSO, PWP, and TNA across the seasons. The findings of this study can be helpful for seasonal predictability in the KSA. Full article
(This article belongs to the Section Climatology)
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Figure 1

Figure 1
<p>The elevation in meters, along with solid circles representing the observation station names in Saudi Arabia. (Data source: USGS satellite topographic dataset).</p>
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<p>Analysis of the changes in ERA5 SAT over the KSA for (<b>a</b>) the summer season (June to August), (<b>b</b>) the winter season (December–January and February). The red lines represent the mean SAT scores for the times 1952–1993 and 1994–2022. Global analysis of the changes in ERA5 SAT of (<b>c</b>) the summer season (December–January and February) and (<b>d</b>) winter season (June to August).</p>
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<p>(<b>a</b>) Summer season trend magnitude (Sen’s slope °C/year) during 1952–1993, (<b>b</b>) same as (<b>a</b>) but for the period 1994–2022. (<b>c</b>) Winter season trend magnitude (Sen’s slope °C/year) during 1952–1993, (<b>d</b>) same as (<b>c</b>) but for the period 1994–2022. Dotted areas show the significance above 99% confidence level.</p>
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<p>(<b>a</b>–<b>c</b>): The Empirical Orthogonal Function (EOF) analysis of SAT temperature for Jun–Aug season during 1952–1993. (<b>d</b>–<b>f</b>) show the corresponding PCs of EOF analyses.</p>
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<p>(<b>a</b>–<b>c</b>) The Empirical Orthogonal Function (EOF) analysis of SAT temperature for Jun–Aug season during 1994–2022. (<b>d</b>–<b>f</b>) show the corresponding PCs of EOFs.</p>
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<p>(<b>a</b>) The leading EOF mode represents the primary pattern, capturing 64.70% of the total variance. (<b>b</b>) The second EOF mode follows, explaining 14.51% of the variance. (<b>c</b>) The third EOF mode contributes to 5.75% of the total variance. (<b>d</b>–<b>f</b>) The associated principal component time series (PC1, PC2, and PC3) correspond to these leading EOF modes.</p>
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<p>(<b>a</b>) The leading EOF mode represents the primary pattern, capturing 54.26% of the total variance. (<b>b</b>) The second EOF mode follows, explaining 19.07% of the variance. (<b>c</b>) The third EOF mode contributes to 7.22% of the total variance. (<b>d</b>–<b>f</b>) The associated principal component time series (PC1, PC2, and PC3) correspond to these leading EOF modes.</p>
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<p>Correlation between PC1 of SAT and MSLP (shaded) and 200 hPa divergent wind fields (vectors) during winter (<b>a</b>) for 1952–1993, (<b>b</b>) for 1994–2022. (<b>c,d</b>) Same as (<b>a</b>,<b>b</b>) but for summer season. The dotted areas show the regions with significance level above 95% by using Student’s <span class="html-italic">t</span>-test.</p>
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<p>(<b>a</b>) Correlation between SAT and ENSO, computed from standardized anomalies of winter seasonal time series spanning 1952–2022. Dotted regions indicate significance levels exceeding 95% confidence, determined using Student’s <span class="html-italic">t</span>-test. (<b>b</b>–<b>f</b>) Same as (<b>a</b>) but for the IOD, PWP, AMM, NAO, and TNA.</p>
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<p>(<b>a</b>) Correlation between SAT and ENSO, computed from standardized anomalies of summer seasonal time series spanning 1952–2022. Dotted regions indicate significance levels exceeding 95% confidence, determined using Student’s <span class="html-italic">t</span>-test. (<b>b</b>–<b>f</b>) same as (<b>a</b>) but for the IOD, PWP, AMM, NAO, and TNA.</p>
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<p>Correlation between climate indices and MSLP (shaded) and 200 hPa divergent wind fields (vectors) during winter from 1994 to 2022. (<b>a</b>) Nino3.4, (<b>b</b>) IOD, (<b>c</b>) PWP, (<b>d</b>) AMM, (<b>e</b>) NAO, and (<b>f</b>) TNA. The dotted areas show the regions with significance level above 95% by using Student’s <span class="html-italic">t</span>-test.</p>
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<p>Correlation between climate indices and MSLP (shaded) and 200 hPa divergent wind fields (vectors) during summer season from 1994 to 2022. (<b>a</b>) Nino3.4, (<b>b</b>) IOD, (<b>c</b>) PWP, (<b>d</b>) AMM, (<b>e</b>) NAO, and (<b>f</b>) TNA. The dotted areas show the regions with significance level above 95% using Student’s <span class="html-italic">t</span>-test.</p>
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<p>The correlation coefficient map between PC1 and global SST for DJF season during (<b>a</b>) 1952–1993, (<b>b</b>) during the period 1994–2022. Stippling denotes regions where the relationship is statistically significant at 95% confidence level based on Student’s <span class="html-italic">t</span>-test.</p>
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<p>The correlation coefficient maps between PC1 and global SST for JJA season during (<b>a</b>) 1952–1993, (<b>b</b>) during the period 1994–2022. Stippling denotes regions where the relationship is statistically significant at 95% confidence level based on Student’s <span class="html-italic">t</span>-test.</p>
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16 pages, 6620 KiB  
Article
Both Biotic and Abiotic Factors Shape the Spatial Distribution of Aboveground Biomass in a Tropical Karst Seasonal Rainforest in South China
by Fang Lu, Bin Wang, Jianxing Li, Dongxing Li, Shengyuan Liu, Yili Guo, Fuzhao Huang, Wusheng Xiang and Xiankun Li
Forests 2024, 15(11), 1904; https://doi.org/10.3390/f15111904 - 29 Oct 2024
Viewed by 434
Abstract
Forest biomass accumulation is fundamental to ecosystem stability, material cycling, and energy flow, and pit lays a crucial role in the carbon cycle. Understanding the factors influencing aboveground biomass (AGB) is essential for exploring ecosystem functioning mechanisms, restoring degraded forests, and estimating carbon [...] Read more.
Forest biomass accumulation is fundamental to ecosystem stability, material cycling, and energy flow, and pit lays a crucial role in the carbon cycle. Understanding the factors influencing aboveground biomass (AGB) is essential for exploring ecosystem functioning mechanisms, restoring degraded forests, and estimating carbon balance in forest communities. Tropical karst seasonal rainforests are species-rich and heterogeneous, yet the impact mechanisms of biotic and abiotic factors on AGB remain incompletely understood. Based on the survey data of a 15 ha monitoring plot in a karst seasonal rainforest in Southern China, this study explores the distribution characteristics of AGB and its intrinsic correlation with different influencing factors. The results show that the average AGB of the plot is 125.7 Mg/ha, with notable variations among habitats, peaking in hillside habitats. Trees with medium and large diameters at breast height (DBH ≥ 10 cm) account for 83.94% of the aboveground biomass (AGB) and are its primary contributors; dominant tree species exhibit higher AGB values. Both biotic and abiotic elements substantially influence AGB, with biotic factors exhibiting the largest influence. Among abiotic factors, topographic factors have a strong direct or indirect influence on AGB, while soil physicochemical properties have the smallest indirect impact. This research provides a comprehensive understanding of AGB distribution and its influencing factors in tropical karst forests (KFs), contributing to the management of carbon sinks in these ecosystems. Full article
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Figure 1
<p>Elevation map, geographical location, and habitat outline of Nonggang 15 ha plot.</p>
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<p>Initial SEM analysis of the impacts of biotic factors, soil physicochemical properties, and topographic factors on the amount of biomass above the ground.</p>
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<p>Distribution of aboveground biomass: map of distribution (<b>a</b>) and across different habitats (<b>b</b>). Lowercase letters denote statistically significant variations between distinct habitats (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The distribution of aboveground biomass analyzed based on different DBH classes (<b>a</b>) and the top 15 species (<b>b</b>).</p>
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<p>Correlation between importance value (<span class="html-italic">IV</span>) and aboveground biomass (AGB). The darkened regions indicate a 95% confidence interval for the models that have been fitted.</p>
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<p>Matrix displaying the precise significance of each ecological element on aboveground biomass. Every row in the dot matrix figure on the right is an environmental component. The single black dot in each column represents the marginal impact of each environmental component. The shared effects between these corresponding environmental elements are indicated by the lines that connect several dots. The variation partitioning process yields the percentage of variance explained by each component, which is shown in the top column graphic. Each environmental element’s individual impact, as determined via hierarchical partitioning, is displayed in the column diagram on the left. Each factor’s value is determined by adding its average shared common effect with other factors to its marginal effect. The notation used for statistical significance is as follows: **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>SEM analysis examining the impact of biotic variables, soil physicochemical parameters, and topographic factors on aboveground biomass (<b>a</b>), as well as the respective contributions of these factors on aboveground biomass (<b>b</b>). The significant effects are represented by black solid arrows (<span class="html-italic">p</span> &lt; 0.05), whereas the non-significant effects are represented by gray solid arrows. The values adjacent to the arrows indicate the standardized coefficients. Abbreviations: StD, structural diversity; InI, individual interactions; Abu, abundance; Ric, richness; Ele, elevation; Slo, slope; Con, convexity. The notation used for statistical significance is as follows: *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Principal Component Analysis (PCA) used to analyze the indices of structural diversity (<b>a</b>) and soil physicochemical property indices (<b>b</b>).</p>
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21 pages, 19883 KiB  
Article
Larval Transport Pathways for Lutjanus peru and Lutjanus argentiventris in the Northwestern Mexico and Tropical Eastern Pacific
by Nicole Reguera-Rouzaud, Guillermo Martínez-Flores, Noé Díaz-Viloria and Adrián Munguía-Vega
Water 2024, 16(21), 3084; https://doi.org/10.3390/w16213084 - 28 Oct 2024
Viewed by 522
Abstract
Understanding how ocean currents influence larval dispersal and measuring its magnitude is critical for conservation and sustainable exploitation, especially in the Tropical Eastern Pacific (TEP), where the larval transport of rocky reef fish remains untested. For this reason, a lagrangian simulation model was [...] Read more.
Understanding how ocean currents influence larval dispersal and measuring its magnitude is critical for conservation and sustainable exploitation, especially in the Tropical Eastern Pacific (TEP), where the larval transport of rocky reef fish remains untested. For this reason, a lagrangian simulation model was implemented to estimate larval transport pathways in Northwestern Mexico and TEP. Particle trajectories were simulated with data from the Hybrid Ocean Coordinate Model, focusing on three simulation scenarios: (1) using the occurrence records of Lutjanus peru and L. argentiventris as release sites; (2) considering a continuous distribution along the study area, and (3) taking the reproduction seasonality into account in both species. It was found that the continuous distribution scenario largely explained the genetic structure previously found in both species (genetic brakes between central and southern Mexico and Central America), confirming that the ocean currents play a significant role as predictors of genetic differentiation and gene flow in Northwestern Mexico and the TEP. Due to the oceanography of the area, the southern localities supply larvae from the northern localities; therefore, disturbances in any southern localities could affect the surrounding areas and have impacts that spread beyond their political boundaries. Full article
(This article belongs to the Special Issue Aquatic Environment and Ecosystems)
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Figure 1
<p>Study area (<b>a</b>). Northwestern Mexico (<b>b</b>), Mexican Tropical Pacific (<b>c</b>), and Central America and Colombia (<b>d</b>). The blue polygons are the counting areas for the connectivity networks (the numbers represent the polygons’ order). The continuous and dashed red lines are the sites where the genetic brakes were found for <span class="html-italic">L. peru</span> and <span class="html-italic">L. argentiventris</span>, respectively [<a href="#B36-water-16-03084" class="html-bibr">36</a>]. Baja California Sur (BCS), Nayarit (NAY), Colima (CMA), Oaxaca (OAX), Panama (PAN), and Colombia (COL).</p>
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<p>Larval seeding points for <span class="html-italic">Lutjanus peru</span> (<b>a</b>), <span class="html-italic">Lutjanus argentiventris</span> (<b>b</b>), and centroids (<b>c</b>).</p>
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<p>Direction and distance traveled by the simulated trajectories obtained with the HYCOM for the zone A (<b>a</b>–<b>d</b>), zone B (<b>e</b>–<b>h</b>), and zone C (<b>i</b>–<b>l</b>). Spring (March (M), April (A), May (M)); Summer (June (J), July (J), August (A)); Autumn (September (S), October (O), November (N)); Winter (December (D), January (J), February (F)).</p>
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<p>Mean seasonal circulation pattern obtained with the HYCOM for zone A during (<b>a</b>) winter, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) autumn from 2017. The color bar represents the velocity of the mean seasonal circulation in m/s, and the arrows indicate the direction.</p>
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<p>Mean seasonal circulation pattern obtained with the HYCOM for zone B during (<b>a</b>) winter, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) autumn from 2017. The color bar represents the velocity of the mean seasonal circulation in m/s, and the arrows indicate the direction.</p>
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<p>Mean seasonal circulation pattern obtained with the HYCOM for zone C during (<b>a</b>) winter, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) autumn from 2017. The color bar represents the velocity of the mean seasonal circulation in m/s, and the arrows indicate the direction.</p>
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<p>Connectivity networks for the continuous distribution scenario (for both species) during winter (<b>a</b>–<b>c</b>) and spring (<b>d</b>–<b>f</b>) for 15 and 30 days of larval dispersal (PLD), respectively. The red dots represent the centroids, the colored lines represent the connectivity networks, and the tick represents the percentage of connectivity between polygons. Baja California (BC), Baja California Sur (BCS), Sonora (SON), Sinaloa (SIN), Nayarit (NAY), Colima (CMA), Guerrero (GUE), Oaxaca (OAX), Panama (PAN), and Colombia (COL).</p>
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<p>Connectivity networks for the occurrence records of <span class="html-italic">Lutjanus peru</span> during winter for 15 (<b>a</b>–<b>c</b>) and 30 (<b>d</b>–<b>f</b>) days of larval dispersal (PLD), respectively. The red dots represent the centroids, the colored lines represent the connectivity networks, and the tick represents the percentage of connectivity between polygons. Baja California (BC), Baja California Sur (BCS), Sonora (SON), Sinaloa (SIN), Nayarit (NAY), Colima (CMA), Guerrero (GUE), Oaxaca (OAX), Panama (PAN), and Colombia (COL).</p>
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<p>Connectivity networks for the occurrence records of <span class="html-italic">Lutjanus argentiventris</span> during summer (<b>a</b>–<b>c</b>) and autumn (<b>d</b>–<b>f</b>) for 15 and 30 days of larval dispersal (PLD), respectively. The red dots represent the centroids, the colored lines represent the connectivity networks, and the tick represents the percentage of connectivity between polygons. Baja California (BC), Baja California Sur (BCS), Sonora (SON), Sinaloa (SIN), Nayarit (NAY), Colima (CMA), Guerrero (GUE), Oaxaca (OAX), Panama (PAN), and Colombia (COL).</p>
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<p>Reproductive season scenario at 15 days of pelagic larval duration (PLD) for <span class="html-italic">Lutjanus peru</span> (<b>a</b>–<b>c</b>) and <span class="html-italic">L. argentiventris</span> (<b>d</b>–<b>f</b>). The red dots represent the centroids, the colored lines represent the connectivity networks, and the tick represents the percentage of connectivity between polygons. Baja California (BC), Baja California Sur (BCS), Sonora (SON), Sinaloa (SIN), Nayarit (NAY), Colima (CMA), Guerrero (GUE), Oaxaca (OAX), Panama (PAN), and Colombia (COL).</p>
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18 pages, 1736 KiB  
Perspective
Leishmaniasis in Humans and Animals: A One Health Approach for Surveillance, Prevention and Control in a Changing World
by Claudia Cosma, Carla Maia, Nushrat Khan, Maria Infantino and Marco Del Riccio
Trop. Med. Infect. Dis. 2024, 9(11), 258; https://doi.org/10.3390/tropicalmed9110258 - 28 Oct 2024
Viewed by 1272
Abstract
Leishmaniasis is classified as a neglected tropical disease (NTD), caused by protozoan parasites of the genus Leishmania, which are transmitted to humans and other animals through the bite of infected female phlebotomine sandflies. There are three forms of the disease: cutaneous leishmaniasis [...] Read more.
Leishmaniasis is classified as a neglected tropical disease (NTD), caused by protozoan parasites of the genus Leishmania, which are transmitted to humans and other animals through the bite of infected female phlebotomine sandflies. There are three forms of the disease: cutaneous leishmaniasis (CL) manifested by ulcers and scars; systemic or visceral leishmaniasis (VL), which can lead to life-threatening complications if left untreated; and mucocutaneous leishmaniasis (MCL), which can destroy the mucous membranes of the nose, mouth and throat. Human leishmaniasis is endemic in many countries across Africa, Asia, Southern Europe, the Middle East, and Central and South America. The interconnection of environmental, animal and human health underlies the spread of the Leishmania parasite. Environmental disruptions, such as climate change, deforestation or urbanisation, but also globalisation and migration, significantly affect the distribution and abundance of sand fly vectors and reservoir hosts. Climate change alters the breeding patterns of sandflies and expands their geographic range; deforestation and misuse of large areas disrupt ecosystems, leading to increased human-vector contact; and urbanisation increases the potential for contact between parties, particularly in densely populated areas. Migration of humans and animals, either through natural migration or, for example, the pet trade and breeding, can facilitate the spread of Leishmania parasites. In addition, socio-economic factors, including poverty and lack of access to healthcare, increase the burden of leishmaniasis in vulnerable populations. Due to this multitude of reasons, the geographic distribution of sandflies has expanded to higher latitudes and altitudes in recent years, with a consequent increase in disease burden. Indeed, despite ongoing challenges in the surveillance systems, data from the last available year have shown an increase in many cases in both humans and dogs. This perspective explores the interconnected factors influencing the spread of leishmaniasis worldwide and the epidemiology of the disease. In addition, it illustrates the importance of integrated strategies in a One Health approach: surveillance, prevention and control of vectors, animals and humans. Full article
(This article belongs to the Special Issue Infectious Disease Prevention and Control: A One Health Approach)
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Figure 1
<p><b>Life cycle of the <span class="html-italic">Leishmania</span> protozoan parasite.</b> (<b>1</b>) The cycle begins when the sand fly inoculates metacyclic (infective) promastigotes into the vertebrate host during a blood meal, along with the fly’s saliva, midgut microbiota, and extracellular vesicles of the parasite. (<b>2</b>) The promastigotes are phagocytosed by macrophages and other mononuclear cells, transforming into amastigotes. (<b>3</b>) The amastigotes divide and infect other cells. (<b>4</b>) The sand fly, during a subsequent blood meal, ingests the infected cells. (<b>5</b>) In the midgut of the sand fly, the amastigotes transform into promastigotes. (<b>6</b>) The promastigotes differentiate into metacyclic forms and colonise the stomodeal valve. Created in BioRender. Cosma, C. (2024). <a href="https://biorender.com/l08n361" target="_blank">https://biorender.com/l08n361</a>.</p>
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<p><b>Visceral leishmaniasis: status of endemicity.</b> Map showing countries endemic for visceral leishmaniasis according to WHO data, highlighting regions where the disease is present and actively transmitted [<a href="#B39-tropicalmed-09-00258" class="html-bibr">39</a>]. Data updated to 2022. Created in BioRender. Cosma, C. (2024). <a href="https://biorender.com/o09z672" target="_blank">https://biorender.com/o09z672</a>.</p>
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<p><b>Cutaneous leishmaniasis: status of endemicity.</b> Map showing countries endemic for cutaneous leishmaniasis according to WHO data, highlighting regions where the disease is present and actively transmitted [<a href="#B40-tropicalmed-09-00258" class="html-bibr">40</a>]. Data updated to 2022 [<a href="#B40-tropicalmed-09-00258" class="html-bibr">40</a>]. Created in BioRender. Cosma, C. (2024). <a href="https://biorender.com/g93i062" target="_blank">https://biorender.com/g93i062</a>.</p>
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<p><b>Leishmaniasis: a One Health approach.</b> Created in BioRender. Cosma, C. (2024). <a href="https://biorender.com/l38m882" target="_blank">https://biorender.com/l38m882</a>.</p>
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22 pages, 7910 KiB  
Article
The Contribution of Moisture Sources of Precipitation to Water Resources Recharge in Semi-Arid Regions
by Hossein Mohammadzadeh, Rogert Sorí and Mojtaba Heydarizad
Atmosphere 2024, 15(11), 1274; https://doi.org/10.3390/atmos15111274 - 24 Oct 2024
Viewed by 423
Abstract
This study investigates the isotopic composition of precipitation in Iran and its moisture sources, offering insights crucial for addressing water recharge and management in semi-arid regions. This study analyzes 150 precipitation events collected from 11 stations across Iran over multiple years. The HYSPLIT [...] Read more.
This study investigates the isotopic composition of precipitation in Iran and its moisture sources, offering insights crucial for addressing water recharge and management in semi-arid regions. This study analyzes 150 precipitation events collected from 11 stations across Iran over multiple years. The HYSPLIT model was used to trace air mass trajectories contributing to these events. The isotopic composition of precipitation from each moisture source was examined to identify their distinct characteristics. Furthermore, the contribution of each air mass to groundwater and surface water recharge was quantified using the Simmr mixing model in R programming language, combining stable isotope data from precipitation and surface/groundwater samples. Precipitation in northern Iran is associated with low d-excess values, indicating moisture from high-latitude sources, particularly the Caspian Sea, while higher d-excess values in the west and south point to moisture mainly from the Persian Gulf and the Mediterranean Sea. Air mass trajectory analysis via the HYSPLIT model identified the dominant pathways of Continental Tropical (CT), Continental Polar (CP), and Mediterranean (MedT) air masses across Iran. Quantitative analysis using the Simmr mixing model revealed that the CT air mass contributes up to 33.6% to groundwater recharge in southern Iran’s karstic regions, while the CP air mass dominates in the north, with up to 46.8% contribution. The MedT air mass, although significant in the west, decreases in influence towards the east. Isotope data from groundwater and surface water sites showed more depleted values than local precipitation, likely due to larger catchment areas. These findings contribute to water management strategies by identifying the variations in moisture sources that influence groundwater and surface water recharge in Iran. Understanding these variations enables the development of targeted strategies for managing water resources in semi-arid regions facing increasing water scarcity. The methodologies applied in this study can be adapted to other regions, providing a valuable framework for sustainable water management in areas where identifying moisture sources is critical. Full article
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Figure 1
<p>The dominant air masses influencing Iran and the studied karstic groundwater and surface water sampling sites across the country.</p>
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<p>The moisture fluxes over Iran for the dry (<b>a</b>) and wet period (<b>b</b>) and the sea surface temperatures (SSTs) in Iran’s neighboring water bodies for the dry (<b>c</b>) and wet period (<b>d</b>).</p>
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<p>The contributions of various air masses to the total precipitation amount and the d-excess values in precipitation across Iran.</p>
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<p>The δ<sup>18</sup>O, δ<sup>2</sup>H, and d-excess characteristics of the precipitation events and precipitation vapor originating from Iran’s main moisture sources (1 = The Arabian Sea; 2 = The Caspian Sea; 3 = Continental (terrestrial) sources; 4 = The Red Sea; 5 = The Black Sea; 6 = The Persian Gulf; 7 = The Mediterranean Sea). The grey symbols represent precipitation, and the black symbols represent vapor.</p>
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<p>Map of R<sup>2</sup> values based on GWR analysis of air mass contributions (CT, MP, MedT, CP) and d-excess values in precipitation across Iran.</p>
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<p>Distributions of δ<sup>18</sup>O, δ<sup>2</sup>H, and d-excess in precipitation, along with the average δ<sup>18</sup>O, δ<sup>2</sup>H, and d-excess values at groundwater and surface water monitoring stations across Iran. Groundwater and surface water stable isotope values are shown in red.</p>
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<p>Plotting the surface water resources across Iran on the local Meteoric Water Lines (MWLs).</p>
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<p>Mapping the surface water resources along the Meteoric Water Lines (MWLs) of Iran’s primary moisture sources: the Caspian Sea, the Persian Gulf, and the Mediterranean Sea.</p>
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<p>Mapping the groundwater samples in the studied karstic zones along the local Meteoric Water Lines (MWLs).</p>
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<p>Mapping the groundwater samples in the studied karstic zones along the Meteoric Water Lines (MWLs) of Iran’s main moisture sources: (<b>a</b>) the Nazarabad anticline, (<b>b</b>) the Ahmadi anticline, (<b>c</b>) the Patagh anticline, (<b>d</b>) the Kardeh anticline, (<b>e</b>) the Esfarayen karstic aquifer, and (<b>f</b>) the Gelvard dam site.</p>
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18 pages, 19830 KiB  
Article
Seasonal Characteristics of Air–Sea Exchanges over the South Coast of Matara, Sri Lanka
by Xuancheng Lu, Yao Luo, Dongxiao Wang, Jinglong Yao, Tilak Priyadarshana, Zhenqiu Zhang and Fenghua Zhou
J. Mar. Sci. Eng. 2024, 12(11), 1903; https://doi.org/10.3390/jmse12111903 - 24 Oct 2024
Viewed by 519
Abstract
Air–sea exchanges play a crucial role in intense weather events over Sri Lanka, particularly by providing the heat and moisture that fuel heavy rainfall. We present a year-round dataset of meteorological observations from the southern shoreline of Sri Lanka in the equatorial Indian [...] Read more.
Air–sea exchanges play a crucial role in intense weather events over Sri Lanka, particularly by providing the heat and moisture that fuel heavy rainfall. We present a year-round dataset of meteorological observations from the southern shoreline of Sri Lanka in the equatorial Indian Ocean for 2017, aiming to investigate its seasonal characteristics and evaluate the performance of reanalysis data in this region. The observations reveal distinct diurnal and seasonal patterns. During the winter and spring, higher shortwave (646.2 W/m2) and longwave radiation (−86.9 W/m2) are coupled with higher temperatures (30.6 °C) and lower humidity (67.4% at noon). In contrast, the Indian summer monsoon period features reduced shortwave (579.8 W/m2) and longwave radiation (−58.6 W/m2), lower temperatures (29.2 °C), higher humidity (over 79.7%), and stronger winds (6.25 m/s). The observations were compared with the ERA5 reanalysis dataset to evaluate the regional performance. The reanalysis data correlated well with the observed data for the radiation, temperature, and sensible heat flux, although notable deviations occurred in terms of the wind speed and latent heat flux. During the impact of Tropical Cyclone Ockhi, the reanalysis data tended to underestimate both the wind speed and precipitation. This dataset will provide vital support for studies on monsoons and coastal atmospheric convection, as well as for model initialization and synergistic applications. Full article
(This article belongs to the Section Marine Environmental Science)
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<p>Location (<b>a</b>,<b>b</b>) and the observation tower (<b>c</b>) at the Ruhuna observation site. The dashed line in (<b>a</b>) represents the mean wind streamlines at 850 hPa for August from 1994 to 2024, with the background based on DEM data from ETOPO2v2 [<a href="#B27-jmse-12-01903" class="html-bibr">27</a>]. The background in (<b>b</b>) is an altigram generated from ASTGTM2 DEM data [<a href="#B28-jmse-12-01903" class="html-bibr">28</a>].</p>
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<p>Seasonal variation of the radiation flux at the Ruhuna site from April 2017 to September 2018: (<b>a</b>) net radiation and (<b>a*</b>) daily variation of seasonal average net radiation; (<b>b</b>) shortwave radiation and (<b>b*</b>) daily variation of seasonal average shortwave radiation; and (<b>c</b>) longwave radiation and (<b>c*</b>) daily variation of seasonal average longwave radiation. Due to power supply issues between 21 January and 24 February, the observation equipment operated solely on solar power, leading to a lack of nighttime observation data during this period.</p>
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<p>Seasonal variation of the meteorology elements at the Ruhuna site from April 2017 to September 2018: (<b>a</b>) humidity and (<b>a*</b>) daily variation of seasonal average humidity; (<b>b</b>) temperature at 2 m and (<b>b*</b>) daily variation of seasonal average temperature; and (<b>c</b>) wind speed and (<b>c*</b>) daily variation of seasonal average wind speed. Due to power supply issues between 21 January and 24 February, the observation equipment operated solely on solar power, leading to a lack of nighttime observation data during this period.</p>
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<p>Seasonal variation of the wind speed at the Ruhuna site from April 2017 to September 2018: (<b>a</b>) spring of 2017; (<b>b</b>) summer of 2017; (<b>c</b>) autumn of 2017; (<b>d</b>) winter of 2017; (<b>e</b>) spring of 2018; (<b>f</b>) summer of 2018; (<b>g</b>) non-summer monsoon period; and (<b>h</b>) summer monsoon period. Colored contours represent the density of the scatter points.</p>
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<p>Monthly precipitation at the Ruhuna site from April 2017 to September 2018.</p>
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<p>Daily variation of the sensible and latent heat fluxes at the Ruhuna site from 24 March 2017 to 15 April 2017: (<b>a</b>) time series; and (<b>b</b>) average daily variation.</p>
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<p>Comparison of the observed data at the Ruhuna site with ERA5-land. (<b>a</b>) Net radiation; (<b>b</b>) net shortwave radiation; (<b>c</b>) net longwave radiation; (<b>d</b>) air temperature; (<b>e</b>) surface wind speed; (<b>f</b>) latent heat flux; (<b>g</b>) sensible heat flux; the red solid line signifies the fitted straight line obtained via the least squares method; and the solid black line represents the 1:1 fit line. Colors represent the density of scatter points.</p>
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<p>Comparison of the observed heat flux and the ERA5 data from 24 March 2017 to 15 April 2017. (<b>a</b>,<b>b</b>) Time series; and (<b>a*</b>,<b>b*</b>) average daily variation. The points indicate the maximum values.</p>
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<p>Tracks of tropical cyclones that made landfall in Sri Lanka from 2011 to 2021; the color denotes the maximum sustained wind speed (m/s) along the track; the background shows the elevation (250 m gradient) and seabed depth (1000 m gradient); Ockhi occurred between 30 November 2017, and 5 December 2017. “One” occurred between 2 January 2014, and 8 January 2014. “Burevi” occurred between 29 November 2020, and 5 December 2020.</p>
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<p>Comparison of the surface meteorological elements with the ERA5-land data under the influence of Ockhi; time series of the (<b>a</b>) temperature; (<b>c</b>) wind speed; (<b>e</b>) 6 h precipitation and scatter of (<b>b</b>) temperature; (<b>d</b>) wind speed; the red solid line signifies the fitted straight line obtained via the least squares method; and the solid black line represents the 1:1 fit line.</p>
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12 pages, 2998 KiB  
Brief Report
Epiphytic Lichens in Salt Flats as Biodiversity Refuges in Reserva Ecológica Arenillas
by Ángel Benítez, Darío Cruz, Fausto López, Nixon Cumbicus, Carlos Naranjo, María Riofrío, Teddy Ochoa-Pérez and Marlon Vega
Diversity 2024, 16(11), 655; https://doi.org/10.3390/d16110655 - 24 Oct 2024
Viewed by 693
Abstract
The mangrove biome is a highly productive system globally, with flora and fauna adapted to significant saline influence, where salt flats coexist alongside these systems, emerging over sands and muds with high salinity and sparse vegetation. The objective of this research is to [...] Read more.
The mangrove biome is a highly productive system globally, with flora and fauna adapted to significant saline influence, where salt flats coexist alongside these systems, emerging over sands and muds with high salinity and sparse vegetation. The objective of this research is to describe, for the first time in Ecuador, the diversity of epiphytic lichens in salt flats in the southern region of Ecuador. Two salt flats were selected where Avicennia germinans and Laguncularia racemosa were the dominant trees with the shrub Batis maritima. A total of 30 species of epiphytic lichens were recorded, with the families Arthoniaceae, Graphidaceae, and Ramalinaceae having the highest number of species, and crustose lichens with photobiont type Trentepohlia showed high richness. The salt flats in the southern region of Ecuador have a high richness of epiphytic lichen species, and the species composition is similar to mangroves, highlighting the importance of their conservation as biodiversity refuges for lichens and consequently other flora and fauna groups. Therefore, epiphytic lichens in salt flats can be used as model organisms to assess their conservation in tropical areas. Full article
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<p>Map of the study area for lichens in the salt flats of the Reserva Ecológica Arenillas in southern Ecuador. Salt flat 1 = S1 and Salt flat 2 = S2.</p>
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<p>Sampling zones: (<b>A</b>) Salt flat 1, (<b>B</b>) Salt flat 2.</p>
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<p>Representative lichen families in two salt flats.</p>
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<p>Lichen species by growth forms in two salt flats.</p>
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<p>Guide to lichen species in different salt flat areas.</p>
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<p>Guide to lichen species in different salt flat areas.</p>
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<p>Guide to lichen species in different salt flat areas.</p>
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<p>Guide to lichen species in different salt flat areas.</p>
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23 pages, 2649 KiB  
Review
Review of Mimusops zeyheri Sond. (Milkwood): Distribution, Utilisation, Ecology and Population Genetics
by Christeldah Mkhonto, Salmina Ngoakoana Mokgehle, Wilfred Otang Mbeng, Luambo Jeffrey Ramarumo and Peter Tshepiso Ndlhovu
Plants 2024, 13(20), 2943; https://doi.org/10.3390/plants13202943 - 21 Oct 2024
Viewed by 628
Abstract
Mimusops zeyheri Sond. (Milkwood) is an indigenous fruit tree species with considerable ecological, cultural, and nutritional significance that remains underexploited. This review synthesizes current knowledge on its distribution, taxonomy, phytochemistry, ethnomedicinal applications, ecological functions, genetic diversity, and biotechnological potential. A systematic literature search, [...] Read more.
Mimusops zeyheri Sond. (Milkwood) is an indigenous fruit tree species with considerable ecological, cultural, and nutritional significance that remains underexploited. This review synthesizes current knowledge on its distribution, taxonomy, phytochemistry, ethnomedicinal applications, ecological functions, genetic diversity, and biotechnological potential. A systematic literature search, spanning 1949 to April 2024, yielded 87 relevant publications from an initial 155. Mimusops zeyheri plays a crucial role in supporting the cultural traditions and economic activities of Indigenous Southern African Communities. Its distribution encompasses South, East, and Southern Tropical Africa, with substantial populations across South African provinces. Ethnomedicinally, various plant parts treat conditions including wounds, gastrointestinal issues, and diabetes. The leaves (34%) and roots (32%) are used, with infusion (33%) and decoction (31%) as primary preparation methods. Oral administration (70%) is the most common, primarily addressing skin conditions (18%). Despite its nutritional richness, a standardized nutrient profile is lacking. Limited genetic diversity studies underscore the need for further research. This study highlights Mimusops zeyheri’s multifaceted importance and research gaps, particularly in other Southern African countries. Future investigations should focus on comprehensive phytochemical analysis, ethnomedicinal validation, ecological conservation, genetic diversity assessment, and biotechnological applications. Multidisciplinary collaborations are recommended to promote sustainable utilization while preserving traditional practices. Full article
(This article belongs to the Special Issue Genetic Resources and Ethnobotany in Aromatic and Medicinal Plants)
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<p>The literature search method used for the selection of articles included in this review.</p>
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<p><span class="html-italic">Mimusops zeyheri</span> plant parts used for managing different ailments and conditions in Southern Africa.</p>
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<p>Methods of preparing <span class="html-italic">Mimusops zeyheri</span> for treatment of different ailments and conditions in Southern Africa.</p>
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<p>The administration method of ethnomedicine from <span class="html-italic">Mimusops zeyheri</span> is used to treat different ailments and conditions in Southern Africa.</p>
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<p>Categories of ailments and conditions treated with <span class="html-italic">Mimusops zeyheri</span> in Southern Africa.</p>
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21 pages, 4116 KiB  
Article
Assessing the Impact of Phase-Change Materials on Enhancing the Thermal Efficiency of Buildings in Tropical Climates
by Tássio Luiz dos Santos, Arthur Santos Silva and Diogo Duarte dos Reis
Energies 2024, 17(20), 5212; https://doi.org/10.3390/en17205212 - 20 Oct 2024
Viewed by 515
Abstract
Civil construction and buildings account for a significant 36% of worldwide energy consumption, contributing to 37% of global CO2 emissions. In Brazil, buildings consume a substantial 51.2% of the nation’s electricity production. Remarkably, approximately one-third of this energy is allocated specifically for [...] Read more.
Civil construction and buildings account for a significant 36% of worldwide energy consumption, contributing to 37% of global CO2 emissions. In Brazil, buildings consume a substantial 51.2% of the nation’s electricity production. Remarkably, approximately one-third of this energy is allocated specifically for maintaining thermal comfort within these structures. The thermal performance of a building has a significant impact on its energy efficiency; in this way, technologies developed to contribute to the energy efficiency of envelopes can directly contribute to the reduction in the building’s overall energy consumption. PCMs are technologies capable of absorbing heat without increasing temperature and can contribute to the better energy performance of envelopes. PCMs are used as a thermal performance solution in cold climate regions, and studies show that they are likely to work in buildings in tropical climates. The objective of this work is to analyze the performance of PCMs in tropical regions of the southern hemisphere, specifically in Brazil, and their behavior according to the constructive system used. Computer simulation contributes to an analysis closer to the reality of the implementation of this technology in these regions. This work is carried out with simulations in the software EnergyPlusTM version 24.1. The results demonstrate that PCMs can effectively contribute to a reduction in energy consumption for the thermal comfort of buildings in tropical climates, demonstrating the possible feasibility of the development of this technology for tropical climates. Full article
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<p>Logical illustration of the simulation sequences.</p>
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<p>Cities’ climate data.</p>
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<p>Cities’ climate data.</p>
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<p>Layout and geometry (measurements in meters).</p>
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<p>Enthalpy functions from the different PCMs.</p>
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<p>Wall layouts. (<b>a</b>) Container wall without PCM; (<b>b</b>) container wall with PCM; (<b>c</b>) EPS wall without PCM; (<b>d</b>) EPS wall with PCM. Note: measurements in centimeters.</p>
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<p>Internal gains rate schedules (people and lights).</p>
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<p>Non-HVAC simulation test.</p>
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<p>HVAC simulation test.</p>
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<p>Difference with HVAC off.</p>
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<p>Difference with HVAC on.</p>
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<p>Results for degrees-hours of cooling/heating (container).</p>
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<p>Thermal loads for cooling/heating (container).</p>
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<p>Results for degrees-time of cooling/heating (SCIP).</p>
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<p>Thermal loads for cooling/heating (SCIP).</p>
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<p>Ratio difference analysis.</p>
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<p>Pearson’s analysis.</p>
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13 pages, 2571 KiB  
Article
The Field Assessment of Quiescent Egg Populations of Aedes aegypti and Aedes albopictus during the Dry Season in Tapachula, Chiapas, Mexico, and Its Potential Impact on Vector Control Strategies
by José Ignacio Navarro-Kraul, Luis Alberto Cisneros Vázquez, Keila Elizabeth Paiz-Moscoso, Rogelio Danis-Lozano, Jesús A Dávila-Barboza, Beatriz Lopez-Monroy, Rosa María Sánchez-Casas, Marco Antonio Domínguez-Galera, Pedro Christian Mis-Avila and Ildefonso Fernandez-Salas
Insects 2024, 15(10), 798; https://doi.org/10.3390/insects15100798 - 14 Oct 2024
Viewed by 780
Abstract
Although integrated management and control programs implement intense control measures for adult, pupal, larval, and breeding sites during outbreaks, there is a lack of studies to understand the role of the vector egg stage in disease dynamics. This study aimed to assess the [...] Read more.
Although integrated management and control programs implement intense control measures for adult, pupal, larval, and breeding sites during outbreaks, there is a lack of studies to understand the role of the vector egg stage in disease dynamics. This study aimed to assess the dry season quiescent Aedes aegypti and Aedes albopictus egg populations in houses and backyards in Tapachula, southern Mexico. Two hundred and fifty ovitraps were placed in 125 homes in the Las Americas neighborhood. A total of 7290 eggs were collected from 211 (84.4%) ovitraps. Only 5667 (77.7%) hatched under insectary water immersion and food supply conditions, with 4031 (71.1%) identified as Ae. aegypti, and 1636 (28.8%) as Ae. albopictus, respectively. The remaining 1623 (22.3%) did not hatch due to Delayed Hatching and/or quiescence tropical stage. Eighty-three larval containers were sampled with desiccated eggs during the dry season; most of them were described as trash waste because larvicides are only used for larger containers of 5–10 L. Evolutionary characteristics for the two species including partial egg hatching, ambient-regulated quiescence, the ability of the embryo to survive for a more extended period intra-seasonally, the egg sticking to inner container walls, demands urgent operational research to achieve successful egg-proof larval container methods. Full article
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<p>(<b>A</b>) Geographic location of the state of Chiapas in the Mexican national territory. (<b>B</b>) Locatable 14.9277, longitude: −92.2602. (<b>C</b>) Geographic limitations of the Las Americas section II and its border with the Coatán River (Google Maps, @ INEGI 2023).</p>
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<p>Distribution of rainfall (mm) and temperatures (°C) in Tapachula, Chiapas, Mexico, from July 2022 to July 2023. Red box represents the months in which field work was carried out (2022–2023).</p>
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<p>Examples of dry-season breeding sites (wet and dry) identified and monitored for the development of <span class="html-italic">Aedes</span> mosquitoes in the Las Americas section II in Tapachula, Chiapas, Mexico. (<b>A</b>) A 5 L plastic container with water, (<b>B</b>) tire with water, (<b>C</b>) 2 L plastic bottle with water, (<b>D</b>) plastic vase with plants, (<b>E</b>) 20 L plastic container with water, and (<b>F</b>) plastic container 5 L with water.</p>
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<p>Individual production of 250 ovitraps placed in 125 houses. A total of 7290 eggs were collected in the paper stripes, but only 5667 hatched. Only 40,131 were identified as adult <span class="html-italic">Aedes aegypti</span>.</p>
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<p>Individual production of 250 ovitraps placed in 125 houses. A total of 7290 eggs were collected in the paper strips, but only 5667 were hatched. Only 1636 were identified as adult <span class="html-italic">Aedes albopictus</span>.</p>
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<p>Adult <span class="html-italic">Aedes aegypti</span> and <span class="html-italic">Aedes albopictus</span> emerging from dry and glued eggs collected above the water line of assorted backyard containers in Tapachula, Chiapas, southern Mexico.</p>
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<p>Average percentage (darkest point) of hatching of <span class="html-italic">Ae. aegypti</span> eggs exposed to different treatments. Kruskal–Wallis test <span class="html-italic">X</span><sup>2</sup> = 60.4; fd = 2; <span class="html-italic">p</span> &lt; 0.001. Differences in all Dwass–Steel–Critchlow–Fligner pairwise comparisons (<span class="html-italic">p</span> &lt; 0.001).</p>
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22 pages, 7116 KiB  
Article
Regional Mean Sea Level Variability Due to Tropical Cyclones: Insights from August Typhoons
by MyeongHee Han, SungHyun Nam and Hak-Soo Lim
J. Mar. Sci. Eng. 2024, 12(10), 1830; https://doi.org/10.3390/jmse12101830 - 14 Oct 2024
Viewed by 689
Abstract
This study investigates the interannual variations in regional mean sea levels (MSLs) of the northeast Asian marginal seas (NEAMS) during August, focusing on the role of typhoon activity from 1993 to 2019. The NEAMS are connected to the Pacific through the East China [...] Read more.
This study investigates the interannual variations in regional mean sea levels (MSLs) of the northeast Asian marginal seas (NEAMS) during August, focusing on the role of typhoon activity from 1993 to 2019. The NEAMS are connected to the Pacific through the East China Sea (ECS) and narrow, shallow straits in the east, where inflow from the southern boundary (ECS), unless balanced by eastern outflow, leads to significant convergence or divergence, as well as subsequent changes in regional MSLs. Satellite altimetry and tide-gauge data reveal that typhoon-induced Ekman transport plays a key role in MSL variability, with increased inflow raising MSLs during active typhoon seasons. In contrast, weak typhoon activity reduces inflow, resulting in lower MSLs. This study’s findings have significant implications for coastal management, as the projected changes in tropical cyclone frequency and intensity due to climate change could exacerbate sea level rise and flooding risks. Coastal communities in the NEAMS region will need to prioritize enhanced flood defenses, early warning systems, and adaptive land use strategies to mitigate these risks. This is the first study to link typhoon frequency directly to NEAMS MSL variability, highlighting the critical role of wind-driven processes in regional sea level changes. Full article
(This article belongs to the Special Issue Air-Sea Interaction and Marine Dynamics)
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<p>Domains of the NEAMS (gray shading), including Area A, where typhoon activity was assessed; Areas B and C, where inflow and outflow occur, respectively (black arrows indicate basic flow transport); and Areas D and E, which are related to time series of zonal and meridional winds. Typhoon tracks are superimposed with symbol sizes and color scales according to maximum sustained wind speeds of 17, 33, 43, 49, 58, and 70 m s<sup>−1</sup> for the months of August from 1993 to 2019. ES, YS, BS, ECS, SCS, SO, and PO denote the East Sea (Sea of Japan), Yellow Sea, Bohai Sea, East China Sea, South China Sea, Sea of Okhotsk, and Pacific Ocean, respectively. KS, TAS, TSS, and SS represent the Korea/Tsushima Strait, Taiwan Strait, Tsugaru Strait, and Soya Straits, respectively.</p>
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<p>Time series of the summer (August) MSLs in the NEAMS region from 1993 to 2019, based on satellite altimetry (red open circles) and tide-gauge observations (black open squares), both adjusted to exclude the inverted barometer effect. The tide-gauge data have been referenced to a common vertical datum to match the MSL with satellite altimetry over the 27-year period. Dashed red and black dotted lines indicate the respective trends for satellite and tide-gauge measurements.</p>
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<p>Time series of the detrended summer NEAMS MSL anomalies (black filled diamond) derived from satellite altimetry data, SLC by Ekman transport anomaly in Area B (red filled square), and SLC by Ekman transport anomaly differences between Areas B and C (red open triangle), derived from ERA5 data from 1993 to 2019. The correlation coefficients between the NEAMS MSL and SLC by Ekman transport anomaly in Area B and between the NEAMS MSL and SLC by Ekman transport anomaly differences between Areas B and C are both 0.65 (<span class="html-italic">p</span>-value &lt; 0.01).</p>
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<p>(<b>a</b>) Time series of detrended summer NEAMS MSL anomalies (black solid line) derived from satellite altimetry data and typhoon occurrence in the ECS (red dashed line) from 1993 to 2019. Summers with relatively high MSL changes (<span class="html-italic">Period H</span>; &gt;2 cm, black solid diamonds) are denoted by orange and green shading, while summers with relatively low MSL changes (<span class="html-italic">Period L</span>; &lt;−2 cm) are denoted by black-filled diamonds. During each summer throughout <span class="html-italic">Period H</span>, three or more typhoons passed through Area A (red solid circles), with the exception of the summers of 2001 and 2002 (green shading,) while two or fewer typhoons occurred during each summer of <span class="html-italic">Period L</span> (red open circles) (correlation coefficient = 0.54; <span class="html-italic">p</span>-value &lt; 0.01). Error bar indicates the positive and negative standard deviations of daily detrended summer NEAMS MSL. Time series of detrended summer (<b>b</b>) ES (green triangle, correlation coefficient = 0.49; <span class="html-italic">p</span> = 0.01) and (<b>c</b>) YS (blue square, correlation coefficient = 0.55; <span class="html-italic">p</span>-value &lt; 0.01) MSL anomalies derived from satellite altimetry data from 1993 to 2019. Error bar indicates the positive and negative standard deviations of daily detrended summer ES and YS MSLs.</p>
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<p>Surface wind stress vectors (black arrows) and typhoon tracks (dotted lines) for (<b>a</b>) composite <span class="html-italic">Period H</span> and (<b>b</b>) composite <span class="html-italic">Period L</span>. The legend for dotted lines can be found in the upper-left corner with criteria for maximum wind speed in m s<sup>−1</sup> similar to <a href="#jmse-12-01830-f001" class="html-fig">Figure 1</a>. Area A and coastlines are denoted by red dashed box and gray lines, respectively.</p>
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<p>Surface wind stress vectors (black arrows) and typhoon tracks (dotted lines) in (<b>a</b>) 2001 and (<b>b</b>) 2002. Area A and coastlines are denoted by a red dashed box and gray lines, respectively.</p>
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<p>Time series of hourly zonal and meridional winds averaged over (<b>a</b>) Area D, summer 2001, and (<b>b</b>) Area E, summer 2002, are plotted using blue squares and red circles, respectively. Monthly mean zonal and meridional winds and zero wind speed lines are represented by the dotted blue and red horizontal lines and solid black lines, respectively. Gray shading represents typhoons Pabuk and Rusa during 19–20 August 2001 and 30–31 August 2002, respectively.</p>
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<p>(<b>a</b>) Correlation map of MSL atmospheric pressure changes related to NEAMS MSL changes during August 1993–2019. In (<b>a</b>), contour intervals are 0.05, and correlations with confidence levels &lt; 90% are discarded. Schematics demonstrate the ocean inflow and outflow (black filled arrows) of the NEAMS and NEAMS MSL anomalies (red and blue) in August driven by (<b>b</b>,<b>c</b>) convergence and (<b>d</b>) divergence during <span class="html-italic">Periods H</span> and <span class="html-italic">L</span>, respectively, related to inflow Ekman transport (black open arrows) induced by wind (blue shaded arrows). In (<b>b</b>), the large L represents the typhoon center. Composite sea level (ADT) anomalies for (<b>b</b>) <span class="html-italic">Period H</span>, (<b>c</b>) summers of 2001 and 2002, and (<b>d</b>) <span class="html-italic">Period L</span> in August obtained from satellite altimeters are indicated using colors.</p>
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<p>Time series of detrended summer NEAMS MSL anomalies derived from satellite altimetry data (black open diamonds, NEAMS) by heat transport (red open circles, HT) and salt transport (blue open squares, ST) differences in anomalies over the ESC (Area B) and the Tsugaru and Soya Straits, by net surface heat flux (magenta asterisk, HF) derived from the Ocean Reanalysis System 5 (ORAS5), and by subtracting MSL by HT, ST, and HF from altimetry MSL (green open triangle, mass transport (MT)) from 1993 to 2019.</p>
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26 pages, 7501 KiB  
Article
Remote Sensing-Based Drought Monitoring in Iran’s Sistan and Balouchestan Province
by Kamal Omidvar, Masoume Nabavizadeh, Iman Rousta and Haraldur Olafsson
Atmosphere 2024, 15(10), 1211; https://doi.org/10.3390/atmos15101211 - 10 Oct 2024
Viewed by 524
Abstract
Drought is a natural phenomenon that has adverse effects on agriculture, the economy, and human well-being. The primary objective of this research was to comprehensively understand the drought conditions in Sistan and Balouchestan Province from 2002 to 2017 from two perspectives: vegetation cover [...] Read more.
Drought is a natural phenomenon that has adverse effects on agriculture, the economy, and human well-being. The primary objective of this research was to comprehensively understand the drought conditions in Sistan and Balouchestan Province from 2002 to 2017 from two perspectives: vegetation cover and hydrology. To achieve this goal, the study utilized MODIS satellite data in the first part to monitor vegetation cover as an indicator of agricultural drought. In the second part, GRACE satellite data were employed to analyze changes in groundwater resources as an indicator of hydrological drought. To assess vegetation drought, four indices were used: Vegetation Health Index (VHI), Vegetation Drought Index (VDI), Visible Infrared Drought Index (VSDI), and Temperature Vegetation Drought Index (TVDI). To validate vegetation drought indices, they were compared with Global Land Data Assimilation System (GLDAS) precipitation data. The vegetation indices showed a strong, statistically significant correlation with GLDAS precipitation data in most regions of the province. Among all indices, the VHI showed the highest correlation with precipitation (moderate (0.3–0.7) in 51.7% and strong (≥0.7) in 45.82% of lands). The output of vegetation indices revealed that the study province has experienced widespread drought in recent years. The results showed that the southern and central regions of the province have faced more severe drought classes. In the second part of this research, hydrological drought monitoring was conducted in fifty third-order sub-basins located within the study province using the Total Water Storage (TWS) deficit, Drought Severity, and Total Storage Deficit Index (TSDI Index). Annual average calculations of the TWS deficit over the period from April 2012 to 2016 indicated a substantial depletion of groundwater reserves in the province, amounting to a cumulative loss of 12.2 km3 Analysis results indicate that drought severity continuously increased in all study basins until the end of the study period. Studies have shown that all the studied basins are facing severe and prolonged water scarcity. Among the 50 studied basins, the Rahmatabad basin, located in the semi-arid northern regions of the province, has experienced the most severe drought. This basin has experienced five drought events, particularly one lasting 89 consecutive months and causing a reduction of more than 665.99 km3. of water in month 1, placing it in a critical condition. On the other hand, the Niskoofan Chabahar basin, located in the tropical southern part of the province near the Sea of Oman, has experienced the lowest reduction in water volume with 10 drought events and a decrease of approximately 111.214 km3. in month 1. However, even this basin has not been spared from prolonged droughts. Analysis of drought index graphs across different severity classes confirmed that all watersheds experienced drought conditions, particularly in the later years of this period. Data analysis revealed a severe water crisis in the province. Urgent and coordinated actions are needed to address this challenge. Transitioning to drought-resistant crops, enhancing irrigation efficiency, and securing water rights are essential steps towards a sustainable future. Full article
(This article belongs to the Section Meteorology)
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<p>(<b>a</b>): The location of Sistan and Balouchestan province in Iran. (<b>b</b>): The studied watersheds (50 grade 3 watersheds). (<b>c</b>): Land cover using MCD12Q1 images for year 2018.</p>
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<p>Flowchart of the study.</p>
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<p>Correlation coefficient between the elements of the indicators.</p>
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<p>Comparison of GLDAS precipitation with Iranshahr synoptic station precipitation.</p>
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<p>The spatial significance level of the correlation (<b>a</b>), and the percentage of each correlation level (<b>b</b>) between the four studied indicators and GLDAS precipitation.</p>
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<p>VHI index in the month of April during the 16 years of the study period. The images are arranged chronologically from left to right, starting in 2002 and ending in 2017.</p>
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<p>VDI index in the month of April during the 16 years of the study period. The images are arranged chronologically from left to right, starting in 2002 and ending in 2017.</p>
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<p>VSDI index in the month of April during the 16 years of the study period. The images are arranged chronologically from left to right, starting in 2002 and ending in 2017.</p>
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<p>TVDI index in the month of April during the 16 years of the study period. The images are arranged chronologically from left to right, starting in 2002 and ending in 2017.</p>
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<p>Calculation of the average deficit of the basins of Sistan and Balouchestan Province.</p>
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<p>Drought severity classification using the TSDI index in the studied station of Sistan and Balouchestan.</p>
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26 pages, 30053 KiB  
Article
Evaluating MPAS-A Performance for Mesoscale Simulation in a Tropical Region: A Case Study of Extreme Heat in Jakarta, Indonesia
by Faiz Rohman Fajary, Han Soo Lee, Vinayak Bhanage, Radyan Putra Pradana, Tetsu Kubota and Hideyo Nimiya
Atmosphere 2024, 15(10), 1202; https://doi.org/10.3390/atmos15101202 - 8 Oct 2024
Viewed by 1470
Abstract
The Model for Prediction Across Scales–Atmosphere (MPAS-A) has been widely used for larger scale simulations, but its performance in mesoscale, particularly in tropical regions, is less evaluated. This study aimed to assess MPAS-A in simulating extreme surface air temperature in Jakarta during the [...] Read more.
The Model for Prediction Across Scales–Atmosphere (MPAS-A) has been widely used for larger scale simulations, but its performance in mesoscale, particularly in tropical regions, is less evaluated. This study aimed to assess MPAS-A in simulating extreme surface air temperature in Jakarta during the hot spells of October 2023 with eight different simulation setups. Several validation metrics were applied to near-surface meteorological variables, land surface temperature (LST), and vertical atmospheric profile. From the eight simulations, MPAS-A captured diurnal patterns of the near-surface variables well, except for wind direction. The model also performed well in LST simulations. Moreover, the biases in the vertical profiles varied with height and were sensitive to the initial/boundary conditions used. Simulations with modified terrestrial datasets showed higher LST and air temperatures over the sprawling urban areas. MPAS-A successfully simulated the extreme event, showing higher air temperatures in southern Jakarta (over 36 °C) compared to the northern part. Negative temperature advection by sea breeze helped lower air temperature in the northern area. This study highlights the role of sea breezes as natural cooling mechanisms in coastal cities. Additionally, MPAS-A is feasible for several applications for urban climate studies and climate projection, although further development is needed. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>(<b>a</b>) Terrain height (m) in the MPAS-A with mesh resolution of 3 km over the study area. Black dots with letters (A–G) show the locations of ground-based observation stations for validation (data source: BMKG, Indonesia). Orange polygon shows province boundary, while black indicates regency/municipality boundary (data source: GIA, Indonesia [<a href="#B41-atmosphere-15-01202" class="html-bibr">41</a>]). (<b>b</b>) Monthly climatology of maximum (red), mean (black), and minimum (blue) temperatures. (<b>c</b>) Maximum values by month of daily maximum, mean, and minimum temperatures in September (solid lines) and October (dashed lines) for each year from 1987 to 2023. (<b>d</b>) Daily (maximum, mean, and minimum) temperatures in October 2023. The data in Tangerang Selatan (marked as D in (<b>a</b>)) are used for (<b>b</b>–<b>d</b>).</p>
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<p>Default (<b>a</b>,<b>c</b>,<b>e</b>) and modified (<b>b</b>,<b>d</b>,<b>f</b>) terrestrial datasets used for MPAS-A inputs, namely land use and land cover (LULC; first row), green vegetation fraction (GVF; second row) in October, and albedo in October. The default datasets are provided on the WRF preprocessing system’s website (WPS). The modified datasets are only applied to the area inside the gray polygon on each map. (<b>g</b>) Albedo distribution over urban and built-up pixels in the study area extracted from the default dataset (<b>e</b>).</p>
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<p>Time series of surface meteorological variables from observation (dot marker symbol and solid line in black) and eight simulations (red, green, blue, and yellow dot or triangle marker symbols and solid or dashed lines in red, green, blue, and yellow) for seven station points (<b>A</b>–<b>G</b>). The shaded areas indicate nighttime. The meteorological variables are air temperature at 2 m (T2m), relative humidity (RH), surface pressure (SP), wind speed (WS), and wind direction (WD).</p>
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<p>Time correlation value (<b>top panel</b>) and mean absolute error (MAE; (<b>bottom panel</b>)) for each surface meteorological variable between observation and simulation output. The two metrics are calculated from combined samples from the seven stations.</p>
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<p>(<b>a</b>–<b>h</b>) Spatial distribution of the time correlation value of land surface temperature (LST) between observation (advanced Himawari imager—AHI) and each simulation output. White areas inside the targeted study area have insignificant correlation values with <span class="html-italic">p</span>-values greater than 5%. (<b>i</b>) Box plots of the correlation coefficients from all grids for each simulation. The black dot with a dashed line shows the area average of the correlation coefficients.</p>
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<p>(<b>a</b>–<b>h</b>) Spatial distribution of MAE of land surface temperature (LST) between observation (advanced Himawari imager—AHI) and each simulation output. (<b>i</b>) Box plots of the MAE from all grids for each simulation. The black dot with a dashed line shows the area average of the MAEs.</p>
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<p>(<b>a</b>–<b>c</b>) Vertical profile of (<b>a</b>) temperature, (<b>b</b>) relative humidity, and (<b>c</b>) mixing ratio in the Soekarno Hatta station (point A in <a href="#atmosphere-15-01202-f001" class="html-fig">Figure 1</a>a) for four time points: 12 UTC 16 October 2023 (red line), 00 UTC 17 October 2023 (green line), 12 UTC 17 October 2023 (blue line), and 00 UTC 18 October 2023 (black line). (<b>d</b>–<b>f</b>) Vertical profile of time average of (<b>d</b>) temperature, (<b>e</b>) relative humidity, and (<b>f</b>) mixing ratio biases in the Soekarno Hatta station for four time points between simulation output and observation.</p>
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<p>The time average of LST (<b>left panel</b>) and T2m (<b>right panel</b>) differences between simulation outputs with modified and default terrestrial input datasets for each grid. Row-wise plots show simulation combinations with the same simulation domain and initial condition but different inputs for the terrestrial datasets (details shown in <a href="#atmosphere-15-01202-t001" class="html-table">Table 1</a>). Regions with no color inside the gray polygon have insignificant values with <span class="html-italic">p</span>-values greater than 5% using a two-tailed Student’s <span class="html-italic">t</span>-test.</p>
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<p>(<b>a</b>–<b>i</b>) Spatial pattern of hourly surface air temperature at 2 m (color shading) and horizontal wind at 10 m (vector) during the day of the extreme event (17 October 2023) from 9 to 17 at local time (LT). Those datasets are outputs from simulation 8. Three colored boxes show the regions of the northern urban area of Jakarta (purple box), the southern urban area of Jakarta (black box), and the agricultural field (green box). Areas inside those boxes are used for area averaging of meteorological variables, as shown in <a href="#atmosphere-15-01202-f010" class="html-fig">Figure 10</a>.</p>
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<p>Hourly variation in area averaging of (<b>a</b>) T2m, (<b>b</b>) radiation budget [net radiation (Q), shortwave radiation (SW), and longwave radiation (LW)], (<b>c</b>) sensible and latent heat, (<b>d</b>) time derivative in T2m, (<b>e</b>) temperature advection, (<b>f</b>) other factors that contributed to the temperature changes over time that are defined by (<b>d</b>) minus (<b>e</b>), (<b>g</b>) zonal component of temperature advection, (<b>h</b>) meridional component of temperature advection, and (<b>i</b>) zonal and meridional wind at 10 m. The reference regions for area averaging are shown in the three boxes in <a href="#atmosphere-15-01202-f009" class="html-fig">Figure 9</a>. The line colors are consistent with the colors of the boxes showing the regions of the northern urban area of Jakarta (purple line), the southern urban area of Jakarta (black line), and the agricultural field (green line). The lines (purple, black, and green) are averages from the eight simulations, and the shadings show the ranges from the eight simulations (ensemble spread). In (<b>a</b>), red and blue dashed lines show T2m at stations A (Soekarno Hatta, located in the northern urban) and D (Banten, located in the southern urban), respectively.</p>
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18 pages, 11141 KiB  
Article
Inter-Model Spread in Representing the Impacts of ENSO on the South China Spring Rainfall in CMIP6 Models
by Xin Yin, Xiaofei Wu, Hailin Niu, Kaiqing Yang and Linglong Yu
Atmosphere 2024, 15(10), 1199; https://doi.org/10.3390/atmos15101199 - 8 Oct 2024
Viewed by 548
Abstract
A major challenge for climate system models in simulating the impacts of El Niño–Southern Oscillation (ENSO) on the interannual variations of East Asian rainfall anomalies is the wide inter-model spread of outputs, which causes considerable uncertainty in physical mechanism understanding and short-term climate [...] Read more.
A major challenge for climate system models in simulating the impacts of El Niño–Southern Oscillation (ENSO) on the interannual variations of East Asian rainfall anomalies is the wide inter-model spread of outputs, which causes considerable uncertainty in physical mechanism understanding and short-term climate prediction. This study investigates the fidelity of 40 models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in representing the impacts of ENSO on South China Spring Rainfall (SCSR) during the ENSO decaying spring. The response of SCSR to ENSO, as well as the sea surface temperature anomalies (SSTAs) over the tropical Indian Ocean (TIO), is quite different among the models; some models even simulate opposite SCSR anomalies compared to the observations. However, the models capturing the ENSO-related warm SSTAs over TIO tend to simulate a better SCSR-ENSO relationship, which is much closer to observation. Therefore, models are grouped based on the simulated TIO SSTAs to explore the modulating processes of the TIO SSTAs in ENSO affecting SCSR anomalies. Comparing analysis suggests that the warm TIO SSTA can force the equatorial north–south antisymmetric circulation in the lower troposphere, which is conducive to the westward extension and maintenance of the western North Pacific anticyclone (WNPAC). In addition, the TIO SSTA enhances the upper tropospheric East Asian subtropical westerly jet, leading to anomalous divergence over South China. Thus, the westward extension and strengthening of WNPAC can transport sufficient water vapor for South China, which is associated with the ascending motion caused by the upper tropospheric divergence, leading to the abnormal SCSR. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction (2nd Edition))
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<p>Climatological distribution of the MAM (March–May) precipitation (shaded, mm day<sup>−1</sup>) and water vapor flux (vector, kg m<sup>−1</sup> s<sup>−1</sup>) over Eastern China from 1979 to 2014 for observations for the MME and individual models.</p>
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<p>Regression map of the MAM precipitation anomalies (shading, mm day<sup>−1</sup>) onto the standardized preceding DJF Niño3.4 index for observations, the MME, and individual models. The stippling denotes statistical significance at the 95% confidence level. The red box indicates the region used to define the SCSR.</p>
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<p>Scatter diagrams of the ENSO-SCSR correlation coefficients (Y−axis) and the interannual standard deviations of the DJF Niño3.4 index (X−axis, °C). Each dot represents the corresponding value for the model identified by the number (<a href="#atmosphere-15-01199-t001" class="html-table">Table 1</a>); “O” and “M” represent observation and MME.</p>
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<p>Regression map of MAM SSTAs (shading, °C) onto the standardized preceding DJF Niño3.4 index in observations, the MME, and individual models. The stippling denotes statistical significance at the 95% confidence level.</p>
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<p>As in <a href="#atmosphere-15-01199-f004" class="html-fig">Figure 4</a>, but for the MAM SSTAs regressed onto the standardized SCSR index. The stippling denotes statistical significance at the 95% confidence level.</p>
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<p>Scatter diagrams of the TIOI standard variations (X−axis) and (<b>a</b>) DJF Niño3.4 standard variations (Y−axis), (<b>b</b>) SCSR standard variations (Y−axis), and (<b>c</b>) ENSO-SCSR correlations (Y−axis) in the CMIP6 models. Each dot represents the corresponding value for the model identified by the number (<a href="#atmosphere-15-01199-t001" class="html-table">Table 1</a>); “O” and “M” represent observation and MME.</p>
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<p>Regression map of MAM precipitation (shading, mm day<sup>−1</sup>) onto the standardized TIOI in observations, the MME, and individual models. The stippling denotes statistical significance at the 95% confidence level. The red box indicates the region used to define the SCSR.</p>
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<p>Scatter diagrams of the TIO-SCSR correlation coefficients (Y−axis) and ENSO-SCSR correlation coefficients (X−axis). The color of each point represents the TIOI-ENSO correlations. “O” and “M” represent the observation and MME.</p>
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<p>Regression map of MAM precipitation anomalies (shading, unit: mm day<sup>−1</sup>) onto the standardized DJF Niño3.4 index for (<b>a</b>) observation, (<b>b</b>) “ENSO-TIO” group, (<b>c</b>) “ENSO-only” group, and (<b>d</b>) “TIO-only” group. The stippling denotes statistical significance at the 95% confidence level.</p>
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<p>Regression map of MAM precipitation anomalies (shading, unit: °C) onto the SSTAs for (<b>a</b>) observation, (<b>b</b>) “ENSO-TIO” group, (<b>c</b>) “ENSO-only” group, and (<b>d</b>) “TIO-only” group. The stippling denotes statistical significance at the 95% confidence level.</p>
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<p>Regression map of MAM 850 hPa (left column) and 200 hPa (right column) wind anomalies (vectors, unit: mm s<sup>−1</sup>) onto the standardized DJF Niño3.4 index for (<b>a</b>,<b>e</b>) observation, (<b>b</b>,<b>f</b>) “ENSO-TIO” group, (<b>c</b>,<b>g</b>) “ENSO-only” group, and (<b>d</b>,<b>h</b>) “TIO-only” group. The red arrow indicates that at least one component of the wind vector passes the 95% significance test. The black arrow indicates that no wind vector passes the 95% significance test.</p>
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<p>Regression map of vertical integral moisture flux (vector, kg m<sup>−1</sup> s<sup>−1</sup>) and moisture flux divergence (shading, 10<sup>−5</sup> kg m<sup>−2</sup> s<sup>−1</sup>) onto the standardized DJF Niño3.4 index for (<b>a</b>) observation, (<b>b</b>) “ENSO-TIO” group, (<b>c</b>) “ENSO-only” group, and (<b>d</b>) “TIO-only” group. The vectors indicate that at least one component of the regressed water vapor flux passes the 95% significance test. The stippling denotes statistical significance at the 95% confidence level.</p>
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11 pages, 471 KiB  
Article
The Impact of Environmental and Housing Factors on the Distribution of Triatominae (Hemiptera, Reduviidae) in an Endemic Area of Chagas Disease in Puebla, Mexico
by Miguel Ortega-Caballero, Maria Cristina Gonzalez-Vazquez, Miguel Angel Hernández-Espinosa, Alejandro Carabarin-Lima and Alia Mendez-Albores
Diseases 2024, 12(10), 238; https://doi.org/10.3390/diseases12100238 - 2 Oct 2024
Viewed by 855
Abstract
Background: Chagas disease (CD), a Neglected Tropical Disease caused by Trypanosoma cruzi, affects millions of people in Latin America and the southern US and spreads worldwide. CD results from close interactions between humans, animals, and vectors, influenced by sociodemographic factors and housing [...] Read more.
Background: Chagas disease (CD), a Neglected Tropical Disease caused by Trypanosoma cruzi, affects millions of people in Latin America and the southern US and spreads worldwide. CD results from close interactions between humans, animals, and vectors, influenced by sociodemographic factors and housing materials. Methods: This study aimed to evaluate how these factors, along with seasonal changes, affect the distribution of CD vectors in an endemic community near Puebla, Mexico, using a cross-sectional survey. A total of 383 people from this area, known for the presence of major vectors such as Triatoma barberi and Triatoma pallidipennis, were surveyed. Results: As a result of the survey, it was found that only 27.4% of respondents knew about CD, and 83.3% owned potential reservoir pets; additionally, the quality of the wall, roof, and floor significantly influenced vector sightings, while the seasonal pattern showed less of an association. Chi-square tests confirmed these associations between vector sightings and housing materials (p < 0.001); vector sightings versus seasonal patterns showed less of an association (p = 0.04), and land use changes did not show an association (p = 0.27). Conclusions: Construction materials play an important role in the sighting of triatomines in homes, so important actions should be taken to improve homes. However, further experimental or longitudinal studies are needed to establish causality. Full article
(This article belongs to the Section Infectious Disease)
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<p>Map showing the location of the Chagas disease endemic area where the surveys were conducted in the state of Puebla, Mexico. The map was designed in QGIS Development Team (2023), QGIS Desktop 3.28.10, Open Source Geospatial Foundation Project: <a href="https://qgis.org/es/site/" target="_blank">https://qgis.org/es/site/</a> (accessed on 14 February 2024).</p>
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