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Search Results (302)

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19 pages, 4359 KiB  
Article
Consistent Coupled Patterns of Teleconnection Between Rainfall in the Ogooué River Basin and Sea Surface Temperature in Tropical Oceans
by Sakaros Bogning, Frédéric Frappart, Valentin Brice Ebode, Raphael Onguene, Gil Mahé, Michel Tchilibou, Jacques Étamé and Jean-Jacques Braun
Water 2025, 17(5), 753; https://doi.org/10.3390/w17050753 - 4 Mar 2025
Viewed by 139
Abstract
This study investigates teleconnections between rainfall in the Ogooué River Basin (ORB) and sea surface temperature (SST) in the tropical ocean basins. The Maximum Covariance Analysis (MCA) is used to determine coupled patterns of SST in the tropical oceans and rainfall in the [...] Read more.
This study investigates teleconnections between rainfall in the Ogooué River Basin (ORB) and sea surface temperature (SST) in the tropical ocean basins. The Maximum Covariance Analysis (MCA) is used to determine coupled patterns of SST in the tropical oceans and rainfall in the ORB, depicting regions and modes of SST dynamics that influence rainfall in the ORB. The application of MCA to rainfall and SST fields results in three coupled patterns with squared covariance fractions of 84.5%, 76.5%, and 77.5% for the Atlantic, Pacific, and Indian tropical basins, respectively. Computation of the correlations of the Savitzky–Golay-filtered resulting expansion coefficients reached 0.65, 0.5 and 0.72, respectively. The SST variation modes identified in this study can be related to the Atlantic Meridional Mode for the tropical Atlantic and the El Niño Southern Oscillation for the tropical Pacific. Over the Indian Ocean, it is a homogeneous mode over the entire basin, instead of the popular dipole mode. Then, the time-dependent correlation method is used to remove any ambiguity on the relationships established from the MCA. Full article
(This article belongs to the Section Water and Climate Change)
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Figure 1

Figure 1
<p>Location map of the ORB with some details on the hydrographic network and topography.</p>
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<p>EOFs and associated temporal PCs from the PCA of rainfall of the ORB. The three leading modes of ORB rainfall variability (1979–2018), represented by EOFs, are depicted in the maps. The corresponding temporal principal components, showing the evolution of each mode over time, are plotted in curves.</p>
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<p>Primary mode of co-variability identified by MCA between monthly precipitation in the ORB and monthly sea SST in the tropical Atlantic Ocean. Maps (<b>a</b>,<b>b</b>) depict the coupled spatial patterns of SST and precipitation anomalies associated with this mode. Graphs (<b>c</b>) show the temporal evolution of the time expansion coefficients associated with these patterns, indicating their relative importance over time.</p>
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<p>Primary mode of co-variability identified by MCA between monthly precipitation in the ORB and monthly sea SST in the tropical Pacific Ocean. Maps (<b>a</b>,<b>b</b>) depict the coupled spatial patterns of SST and precipitation anomalies associated with this mode. Graphs (<b>c</b>) show the temporal evolution of the time expansion coefficients associated with these patterns, indicating their relative importance over time.</p>
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<p>Primary mode of co-variability identified by MCA between monthly precipitation in the ORB and monthly sea SST in the tropical Indian Ocean. Maps (<b>a</b>,<b>b</b>) depict the coupled spatial patterns of SST and precipitation anomalies associated with this mode. Graphs (<b>c</b>) show the temporal evolution of the time expansion coefficients associated with these patterns, indicating their relative importance over time.</p>
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<p>Moving correlation between AMM and the leading temporal PCs of rainfall in the ORB. Blue lines show 12-month rolling correlations with the principal components derived from the three leading EOFs. Gray dots indicate statistically significant correlations.</p>
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<p>Moving correlations between ENSO and leading temporal PCs of rainfall in the ORB. Blue lines show 12-month rolling correlations with the principal components derived from the three leading EOFs. Gray dots indicate statistically significant correlations.</p>
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<p>Moving correlations between IDM and leading temporal PCs of rainfall in the ORB. Blue lines show 12-month rolling correlations with the principal components derived from the three leading EOFs. Gray dots indicate statistically significant correlations.</p>
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17 pages, 4185 KiB  
Article
The Spatial Distribution Dynamics of Shark Bycatch by the Longline Fishery in the Western and Central Pacific Ocean
by Shengyao Xia, Jiaqi Wang, Xiaodi Gao, Yiwei Yang and Heyang Huang
J. Mar. Sci. Eng. 2025, 13(2), 315; https://doi.org/10.3390/jmse13020315 - 8 Feb 2025
Viewed by 886
Abstract
Shark bycatch represents a substantial issue in the management of oceanic fisheries. Utilizing data on shark bycatch from the longline fishery, as released by the Western and Central Pacific Fisheries Commission, this study applied the boosted regression tree model to examine the impact [...] Read more.
Shark bycatch represents a substantial issue in the management of oceanic fisheries. Utilizing data on shark bycatch from the longline fishery, as released by the Western and Central Pacific Fisheries Commission, this study applied the boosted regression tree model to examine the impact of environmental factors on the bycatch per unit effort (BPUE) of key bycatch species, as well as to predict the spatial distribution dynamics of both BPUE and bycatch risk (BR). The findings emphasize that the oxygen concentration, sea surface temperature, and chlorophyll-a concentration are paramount to sharks’ BPUE. Furthermore, the study compared the variations in environmental preferences across diverse shark species, pinpointing key environmental attributes defining the ecological niches of distinct shark populations. The spatial predictions identified the hotspots of BPUE and BR for the bigeye thresher shark (Alopias superciliosus), longfin mako (Isurus paucus), silky shark (Carcharhinus falciformis), and oceanic whitetip shark (Carcharhinus longimanus) in tropical latitudes (10° S to 15° N), and for the blue shark (Prionace glauca) and shortfin mako (Isurus oxyrinchus) in temperate zones (south of 30° S or north of 30° N). The geometric center analysis indicated that all shark species exhibited large annual fluctuations in BPUE and BR, and most populations displayed significant shifting trends. Several grids (5° × 5°) were identified as high-risk areas due to their considerable contribution to bycatch. Furthermore, the geometric centers of BR were observed to shift eastward towards equatorial waters, compared to the geometric centers of BPUE. This underscores the necessity of considering factors beyond BPUE when identifying critical areas for the implementation of area-specific bycatch mitigation measures. The insights derived from this study can enhance and support the development and enforcement of targeted area-based fishery management initiatives. Full article
(This article belongs to the Section Marine Ecology)
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Figure 1
<p>The relative contribution (%) to the model’s explanatory capacity of the six environmental variables.</p>
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<p>The predicted spatial distribution of the annual average BPUE (ind./1000 hooks). Note: the “N” and “S” in the plot represent the North and South Pacific stocks, respectively. Below is the same.</p>
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<p>The spatial distribution of the annual average BR.</p>
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<p>The spatial distribution of the four BR levels corresponding to the eight shark stocks.</p>
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<p>Annual variations in the geometric centers of BPUE and BR for the eight shark stocks from 2013 to 2021. The directions indicated by the solid and dashed arrows represent the trends in changes for BPUE and BR, respectively.</p>
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<p>The average disparity of the geometric centers between BPUE and BR.</p>
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17 pages, 3043 KiB  
Communication
Invasion of the Atlantic Ocean and Caribbean Sea by a Large Benthic Foraminifer in the Little Ice Age
by Edward Robinson and Thera Edwards
Diversity 2025, 17(2), 110; https://doi.org/10.3390/d17020110 - 2 Feb 2025
Viewed by 827
Abstract
The larger benthic foraminifera is a group of marine protists harbouring symbiotic algae, that are geographically confined to shallow tropical and subtropical waters, often associated with coral reefs. The resulting controls on availability of habitat and rates of dispersion make these foraminifers, particularly [...] Read more.
The larger benthic foraminifera is a group of marine protists harbouring symbiotic algae, that are geographically confined to shallow tropical and subtropical waters, often associated with coral reefs. The resulting controls on availability of habitat and rates of dispersion make these foraminifers, particularly the genus Amphistegina, useful proxies in the study of invasive marine biota, transported through hull fouling and ballast water contamination in modern commercial shipping. However, there is limited information on the importance of these dispersal mechanisms for foraminifers in the Pre-Industrial Era (pre-1850) for the Atlantic and Caribbean region. This paper examines possible constraints and vectors controlling the invasion of warm-water taxa from the Indo-Pacific region to the Atlantic and Caribbean region. Heterostegina depressa, first described from St. Helena, a remote island in the South Atlantic, provides a test case. The paper postulates that invasions through natural range expansion or ocean currents were unlikely along the possible available routes and hypothesises that anthropogenic vectors, particularly sailing ships, were the most likely means of transport. It concludes that the invasion of the Atlantic by H. depressa was accomplished within the Little Ice Age (1350–1850 C.E.), during the period between the start of Portuguese marine trade with east Africa in 1497 and the first description of H. depressa in 1826. This hypothesis is likely applicable to other foraminifers and other biota currently resident in the Atlantic and Caribbean region. The model presented provides well-defined parameters that can be tested using methods such as isotopic dating of foraminiferal assemblages in cores and genetic indices of similarity of geographic populations. Full article
(This article belongs to the Special Issue Ecology and Paleoecology of Atlantic and Caribbean Coral Reefs)
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Figure 1
<p><span class="html-italic">Heterostegina depressa</span> d’Orbigny, Recent, Discovery Bay, Jamaica. (Edward Robinson collection, donated by Thomas. F. Goreau).</p>
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<p>Caribbean and Central American localities for Heterostegina depressa. See <a href="#app1-diversity-17-00110" class="html-app">Appendix A</a> <a href="#diversity-17-00110-t0A1" class="html-table">Table A1</a>.</p>
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<p>Introduction and spread of shipping activity in the Atlantic and Caribbean at the end of the 15th Century. Bold black line—the Swahili Coast of east Africa. Red lines—routes opened by Portuguese traders to the Swahili Coast and Indo-Pacific [<a href="#B98-diversity-17-00110" class="html-bibr">98</a>] (p. 390). Blue area—approximate area of Spanish trading and expansion to the Caribbean and Central America, based on the routes of Columbus’ four voyages [<a href="#B103-diversity-17-00110" class="html-bibr">103</a>] (p. 80).</p>
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<p>Suggested route of the invasion of the Caribbean by <span class="html-italic">Heterostegina depressa</span>, Blue line -. Circular Points, locations yielding Recent <span class="html-italic">H. depressa</span> based on [<a href="#B7-diversity-17-00110" class="html-bibr">7</a>] with the additions of Bermuda and some Caribbean localities (Panama, CP). Yellow area—routes of the Atlantic Slave Trade as described in [<a href="#B103-diversity-17-00110" class="html-bibr">103</a>,<a href="#B110-diversity-17-00110" class="html-bibr">110</a>]. Dashed lines—approximate limits of the tropical zone with year-long SSTs above 20 °C, based on data from [<a href="#B111-diversity-17-00110" class="html-bibr">111</a>]. Other symbols are the same as <a href="#diversity-17-00110-f003" class="html-fig">Figure 3</a>.</p>
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23 pages, 27902 KiB  
Article
Spatio-Temporal Characteristics of Climate Extremes in Sub-Saharan Africa and Potential Impact of Oceanic Teleconnections
by Lormido Ernesto Zita, Flávio Justino, Carlos Gurjão, James Adamu and Manuel Talacuece
Atmosphere 2025, 16(1), 86; https://doi.org/10.3390/atmos16010086 - 15 Jan 2025
Viewed by 1563
Abstract
Sub-Saharan Africa (SSA) is a region vulnerable to extreme weather events due to its low level of adaptive capacity. In recent decades, SSA has been punctuated by more intense climatic phenomena that severely affect its population. Therefore, this study evaluates the performance of [...] Read more.
Sub-Saharan Africa (SSA) is a region vulnerable to extreme weather events due to its low level of adaptive capacity. In recent decades, SSA has been punctuated by more intense climatic phenomena that severely affect its population. Therefore, this study evaluates the performance of the ERA5 and CHIRPS datasets, and the spatio-temporal evolution of extreme weather indices and their potential relationship/response to climate variability modes in the Pacific, Indian, and Atlantic oceans, namely, the El Niño−Southern Oscillation, Indian Ocean Dipole, and Tropical Atlantic Variability (ENSO, IOD, and TAV). The CHIRPS dataset showed strong positive correlations with CPC in spatial patterns and similarity in simulating interannual variability and in almost all seasons. Based on daily CHIRPS and CPC data, nine extreme indices were evaluated focusing on regional trends and change detection, and the maximum lag correlation method was applied to investigate fluctuations caused by climate variability modes. The results revealed a significant decrease in total precipitation (PRCPTOT) in north−central SSA, accompanied by a reduction in Consecutive Wet Days (CWDs) and maximum 5-day precipitation indices (RX5DAYS). At the same time, there was an increase in Consecutive Dry Days (CDDs) and maximum rainfall in 1 day (RX1DAY). With regard to temperatures, absolute minimums and maximums (TNn and TXn) showed a tendency to increase in the center−north and decrease in the south of the SSA, while daily maximums and minimums (TXx and TNx) showed the opposite pattern. The IOD, TAV, and ENSO modes of climate variability influence temperature and precipitation variations in the SSA, with distinct regional responses and lags between the basins. Full article
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Figure 1

Figure 1
<p>Comparative analysis of the monthly rainfall deviation (mm/month) between CPC and CHIRPS (<b>a</b>); CPC and ERA5 (<b>b</b>); CHIRPS and ERA5 (<b>c</b>); KGE between CPC and CHIRPS (<b>d</b>); CPC and ERA5 (<b>e</b>); and CHIRPS and ERA5 (<b>f</b>) over the period 1981–2023. The star symbol (★) corresponds to statistical significance at the 95% confidence interval.</p>
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<p>Annual variation in precipitation obtained from spatial products such as ERA 5, CHIRPS, and CPC (considered real) in the Congo (<b>a</b>), Central East Coast (<b>b</b>), North West Coast (<b>c</b>), and Orange (<b>d</b>) river basins from 1981 to 2023.</p>
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<p>Interannual variability of precipitation in the Congo (<b>a</b>–<b>d</b>) and Central East Coast (<b>e</b>–<b>h</b>) catchment over the period from 1981 to 2023, divided into four seasons: DJF (summer; (<b>a</b>,<b>e</b>)), MAM (fall; (<b>b</b>,<b>f</b>)), JJA (winter; (<b>c</b>,<b>g</b>)), and SON (spring; (<b>d</b>,<b>h</b>)).</p>
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<p>Interannual variability of precipitation in the Coast West (<b>a</b>–<b>d</b>) and Orange watershed (<b>e</b>–<b>h</b>) catchment over the period from 1981 to 2023, divided into four seasons: DJF (summer; (<b>a</b>,<b>e</b>)), MAM (fall; (<b>b</b>,<b>f</b>)), JJA (winter; (<b>c</b>,<b>g</b>)), and SON (spring; (<b>d</b>,<b>h</b>)).</p>
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<p>Spatial distribution of extreme precipitation based on the CHIRPS dataset: PRCPTOT (<b>a</b>), CWDs (<b>b</b>), CDDs (<b>c</b>), RX1DAY (<b>d</b>), RX5DAYS (<b>e</b>); and temperature indices based on the CPC dataset: TNn (<b>f</b>), TXn (<b>g</b>), TNx (<b>h</b>), TXx (<b>i</b>), for sub-Saharan Africa for the period 1981–2023.</p>
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<p>Spatial distribution of trends and statistical significance of extreme precipitation based on the CHIRPS dataset: PRCPTOT (<b>a</b>), CWDs (<b>b</b>), CDDs (<b>c</b>), RX1DAY (<b>d</b>), and RX5DAYS (<b>e</b>); and air temperature indices based on the CPC dataset: TNn (<b>f</b>), TNx (<b>g</b>), TXx (<b>h</b>), and TXn (<b>i</b>), during the period 1981–2023. Shaded areas are significant at the 95% level.</p>
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<p>Maximum correlation between ENSO and CDDs (<b>a</b>), IOD and CDDs (<b>b</b>), TAV and CDDs (<b>c</b>). ENSO and CWDs (<b>d</b>), IOD and CWDs (<b>e</b>), TAV and CWDs (<b>f</b>), ENSO and RX1DAY (<b>g</b>), IOD and RX1DAY (<b>h</b>), TAV and RX1DAY (<b>i</b>). ENSO and PRCPTOT (<b>j</b>), IOD and PRCPTOT (<b>k</b>), and TAV and PRCPTOT (<b>l</b>).</p>
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<p>Same as <a href="#atmosphere-16-00086-f007" class="html-fig">Figure 7</a> but for ENSO and TNn (<b>a</b>), IOD and TNn (<b>b</b>), TAV and TNn (<b>c</b>). ENSO and TXx (<b>d</b>), IOD and TXx (<b>e</b>), TAV and TXx (<b>f</b>), ENSO and TNx (<b>g</b>), IOD and TNx (<b>h</b>), TAV and TNx (<b>i</b>). ENSO and TXn (<b>j</b>), IOD and TXn (<b>k</b>), TAV and TXn (<b>l</b>).</p>
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<p>Spatio−temporal averaged time−series of CDDs and CWDs (<b>a</b>–<b>d</b>) based on the CHIRPS dataset; TNn and TXx (<b>e</b>–<b>h</b>) based on the CPC dataset during the period 1981–2023 for individual river basin depicted in the regional SSA map (top right). The star symbol (★) corresponds to statistical significance at the 95% confidence interval.</p>
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<p>The lagged cross-correlations between the ENSO and CDDs (<b>a</b>), IOD and CDDs (<b>b</b>), TAV and CDDs (<b>c</b>). ENSO and CWDs (<b>d</b>), IOD and CWDs (<b>e</b>), TAV and CWDs (<b>f</b>), ENSO and RX1DAY (<b>g</b>), IOD and RX1DAY (<b>h</b>), TAV and RX1DAY (<b>i</b>). ENSO and PRCPTOT (<b>j</b>), IOD and PRCPTOT (<b>k</b>), TAV and PRCPTOT (<b>l</b>).</p>
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<p>Same as <a href="#atmosphere-16-00086-f0A1" class="html-fig">Figure A1</a> but for ENSO and TNn (<b>a</b>), IOD and TNn (<b>b</b>), TAV and TNn (<b>c</b>). ENSO and TXx (<b>d</b>), IOD and TXx (<b>e</b>), TAV and TXx (<b>f</b>), ENSO and TNx (<b>g</b>), IOD and TNx (<b>h</b>), TAV and TNx (<b>i</b>). ENSO and TXn (<b>j</b>), IOD and TXn (<b>k</b>), TAV and TXn (<b>l</b>).</p>
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17 pages, 3620 KiB  
Article
Extending Satellite Predictions of Coral Disease Outbreak Risk to Non-Seasonal Coral Reef Regions
by Momoe Yoshida and Scott F. Heron
Remote Sens. 2025, 17(2), 262; https://doi.org/10.3390/rs17020262 - 13 Jan 2025
Viewed by 510
Abstract
Coral disease outbreaks have increased in frequency and extent worldwide since the 1970s, coinciding with the rapid increase in ocean warming. Summer and winter temperature-based metrics have proven effective in predicting coral disease outbreaks in seasonal coral reef regions. However, their utility is [...] Read more.
Coral disease outbreaks have increased in frequency and extent worldwide since the 1970s, coinciding with the rapid increase in ocean warming. Summer and winter temperature-based metrics have proven effective in predicting coral disease outbreaks in seasonal coral reef regions. However, their utility is unknown in non-seasonal coral reef areas. Here, a new methodology, independent of seasonal patterns, is developed for application in both seasonal and non-seasonal coral reef regions. Percentile-based metric thresholds were defined from seasonal equivalents in the Great Barrier Reef (GBR) and tested in seasonal and non-seasonal coral reef regions of the tropical Pacific Ocean. Between new and existing methodologies, median differences of 0.00 °C (thresholds) and 0.00 °C-weeks (metrics) for Hot Snap and Cold Snap; and 0.01 °C (threshold) and −0.17 °C-weeks (metric) for Winter Condition were observed among reef pixels of the GBR. The new methodology shows strong consistency with the existing tools used for seasonal regions (e.g., R2 = 0.811–0.903; GBR case studies). Comparisons of the new metrics with disease observations were constrained by the limited availability of disease data; however, the comparisons undertaken suggest predictive capability in non-seasonal regions. To establish robust correlations, further direct comparisons of the new metrics with disease data across various non-seasonal regions and timeframes are essential. With ocean warming projected to persist in the coming decades, improving the predictive tools used to assess ecological impacts is necessary to support effective coral reef management. Full article
(This article belongs to the Section Coral Reefs Remote Sensing)
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Graphical abstract

Graphical abstract
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<p>Coral reef regions within the FORE-C project: (<b>1</b>) the Great Barrier Reef (light grey indicates 4412 reef-containing pixels within the coordinate range of [140°E–155°E, 25°S–10°S]); (<b>2</b>) Guam and Northern Mariana Islands; (<b>3</b>) Howland (a) and Jarvis (b) Islands (noting the very sparse nature of reef locations). Locations where 5 km × 5 km pixel SST data are used in subsequent figures are highlighted (light-blue outline); letters (a,b,c) are provided to cross-reference specific pixels. The global map shows these and other FORE-C regions (grey boxes) from north to south as follows: Wake Atoll, the Hawaiian archipelago, and Johnston Atoll; Palmyra Atoll and Kingman Reef; and American Samoa (including Swains Island). The location of Baker Island (0.194°N, 176.477°W) is shown as a single dot in panel (<b>3</b>) just to the south of Howland Island (<b>3</b>a).</p>
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<p>Hot Snap, Cold Snap, and Winter Condition metrics (°C-weeks) for a sample SST time-series extracted from a 5 km × 5 km reef pixel at 153°4′E, 24°22′S for the period from 01 May 2001 to 01 November 2002 (for the panel label, refer to <a href="#remotesensing-17-00262-f001" class="html-fig">Figure 1</a>). The Hot Snap metric (area in red) accumulates when temperature exceeds the summer AV plus one summer SD (red dashed line). The Cold Snap metric (area in blue at left) accumulates when temperature drops below the winter AV minus one winter SD (bottom blue dashed line). The Winter Condition metric (area in purple at right) accumulates (1) within the three winter months (period of accumulation), and/or (2) when temperature is equal to or below the winter AV plus one winter SD (top purple dashed line). As the wintertime values combine with the subsequent summer conditions to predict disease risk [<a href="#B5-remotesensing-17-00262" class="html-bibr">5</a>], the Hot Snap of 3.02 °C-weeks and the Cold Snap of −1.65 °C-weeks predict disease outbreak risk in 2002, while the Winter Condition of −2.48 °C-weeks informs disease prediction for 2003. Red and blue arrows along the time axis indicate the summer reset month (i.e., October for Hot Snap) and the winter reset month (i.e., May for Cold Snap and Winter Condition), respectively; this is when each metric is initialised to 0 °C-weeks each year. The second axis (at right) illustrates the percentile value corresponding to each of the SST thresholds specified in this pixel.</p>
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<p>(<b>a</b>) Variabilities in percentile values corresponding to the five AV- and SD-based thresholds for the 21-year period 1985–2005. Green lines are the median percentiles across reef-containing pixels in the Great Barrier Reef. Boxes indicate the interquartile range (IQR), and whiskers extend to the farthest data point within 1.5 × IQR from the box. Circles indicate outliers beyond the extent of the whiskers. (<b>b</b>) Spatial variations in differences in Hot Snap thresholds (97th percentile—summer AV+SD). Threshold values (°C) were derived from the daily SST between 1985 and 2005 for each pixel. (<b>c</b>) Spatial variations in the difference in Hot Snap for 2001/2002 (percentile-based—summer AV+SD). Histograms in the second and third panels show the distribution of thresholds and metrics, respectively, among 4412 reef-containing pixels (colours correspond to spatial variations).</p>
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<p>SST time-series (2000–2020) at seasonal (top-left; 1a), weakly seasonal (bottom-left; 2), and non-seasonal (right panels; 3a,3b) exemplar pixels (panel labels refer to <a href="#remotesensing-17-00262-f001" class="html-fig">Figure 1</a>). Pale red, pale purple, and light blue lines are the existing AV- and SD-based thresholds (dashed for summer AV+SD, winter AV+SD, and winter AV–SD; solid for winter AV), while red, purple, and blue lines indicate the new percentile-based thresholds (97th, 28th, 16th, and 5th percentiles).</p>
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<p>Time-series at Jarvis Island (2000–2020; panel labels refer to the pixel location in <a href="#remotesensing-17-00262-f001" class="html-fig">Figure 1</a>.3b) of the (<b>a</b>) existing AV- and SD-based and the (<b>b</b>) new percentile-based Hot Snap (red), Cold Snap (blue) and Winter Condition (purple) metrics. Red and blue arrows indicate features referred to in the text.</p>
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<p>Change in the reset timing for the ‘summer’ (<b>left</b>) and ‘winter’ (<b>right</b>) metrics (absolute number of months) between the new percentile-based and existing AV- and SD-based methodologies at reef pixels (shown in <a href="#remotesensing-17-00262-f001" class="html-fig">Figure 1</a>). The colour intensity indicates the percentage of pixels in a region with a certain difference. Analyses were conducted for the baseline period 1985–2005. Numbers of reef-containing pixels within each of the regions were: the Great Barrier Reef, 4412 pixels; the Hawaiian archipelago, 630 pixels; Johnston Atoll, 23 pixels; Guam and Northern Mariana Islands, 155 pixels; American Samoa, 56 pixels; Wake Atoll, 6 pixels; Palmyra Atoll and Kingman Reef, 18 pixels; Howland and Baker Islands, 12 pixels; and Jarvis Island, 2 pixels.</p>
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<p>Time-series of the new percentile-based Hot Snap (red), Cold Snap (blue), and Winter Condition (purple) for (<b>left</b>) Howland Island, 2012–2018; and (<b>right</b>) Piti, Guam, 2012–2014 (for the panel labels, refer to <a href="#remotesensing-17-00262-f001" class="html-fig">Figure 1</a>). Black dots indicate the observed mean prevalence of white syndrome.</p>
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12 pages, 2374 KiB  
Article
Populations of the Invasive Mussel Mytella strigata in China Showed Lower Genetic Diversity in Autumn than in Spring
by Peizhen Ma, Chenxia Zuo, Shaojing Yan, Xiangyu Wu, Xiaojie Ma, Yi Zhu and Zhen Zhang
Biology 2025, 14(1), 16; https://doi.org/10.3390/biology14010016 - 27 Dec 2024
Viewed by 693
Abstract
Native to tropical America, the charru mussel, Mytella strigata, has been spreading rapidly in the West Pacific Ocean, including the South China Sea. In order to study the adaptive evolution of M. strigata and examine the present status of invasion in China, [...] Read more.
Native to tropical America, the charru mussel, Mytella strigata, has been spreading rapidly in the West Pacific Ocean, including the South China Sea. In order to study the adaptive evolution of M. strigata and examine the present status of invasion in China, the mitochondrial nad2 gene fragment was employed to analyze the genetic variations of seven populations sampled in both spring and autumn 2023. Results showed that all the populations had high haplotype diversity (>0.5) and low nucleotide diversity (<0.005), suggesting the ongoing rapid expansion following a genetic bottleneck. The Zhanjiang population had the highest genetic diversity in spring with 22 haplotypes, 37 polymorphic sites, and haplotype diversity, nucleotide diversity, and the average number of nucleotide differences being 0.911, 0.00623, and 4.341, respectively. However, in autumn, the Shanwei population had the most haplotypes (11) and polymorphic sites (19), with the highest haplotype diversity value of 0.891, while the Qunjian population had the highest nucleotide diversity (0.00392) and average number of nucleotide differences (2.809). Combining geographic populations by seasons confirmed lower genetic diversity in autumn compared to spring, evidenced by fewer haplotypes and polymorphic sites, reduced haplotype diversity and nucleotide diversity, and lower genetic distance within populations. These findings provided evidence for understanding the molecular characteristics of M. strigata population expansion in China. Full article
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<p>Chinese populations of <span class="html-italic">Mytella strigata</span> sampled in this study.</p>
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<p>The bar chart of genetic diversity parameters for spring and autumn populations of <span class="html-italic">Mytella strigata</span>. (<b>A</b>–<b>E</b>) represent the differences in <span class="html-italic">h</span>, <span class="html-italic">S</span>, <span class="html-italic">Hd</span>, <span class="html-italic">Pi</span>, and <span class="html-italic">K</span> parameters between the spring and autumn populations of <span class="html-italic">Mytella strigata</span>, respectively.</p>
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<p>A TCS Network of <span class="html-italic">Mytella strigata</span> populations collected in spring 2023.</p>
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<p>A TCS Network of <span class="html-italic">Mytella strigata</span> populations collected in autumn 2023.</p>
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<p>A TCS Network of spring and autumn populations of <span class="html-italic">Mytella strigata</span>.</p>
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18 pages, 1409 KiB  
Article
Population Dynamics of the Crocodile Shark, Pseudocarcharias kamoharai, in the Tropical Equatorial Pacific Ocean, Ecuador
by Marcos Douglas Calle-Morán, Eugenio Alberto Aragón-Noriega, Ana Rosa Hernández-Téllez, Emigdio Marín-Enríquez, Javier Tovar-Ávila, Juan Francisco Arzola-González and Jorge Payán-Alejo
Fishes 2025, 10(1), 5; https://doi.org/10.3390/fishes10010005 - 26 Dec 2024
Viewed by 1295
Abstract
The objectives of this study were to determine the rates of natural mortality (M), fishing mortality (F), total mortality (Z), the exploitation rates (E), as well as the biological reference points (BRPs) and [...] Read more.
The objectives of this study were to determine the rates of natural mortality (M), fishing mortality (F), total mortality (Z), the exploitation rates (E), as well as the biological reference points (BRPs) and the annual removal rates (R) of the crocodile shark, Pseudocarcharias kamoharai, in the Ecuadorian Pacific. Thirty similar and different models were applied to determine all these rates. These equations were obtained from studies on teleost and chondrichthyan fish. The biological parameters, including age, growth, longevity, and reproduction, were obtained from the specialized literature based on the biology of P. kamoharai in Ecuadorian waters. These biological parameters were used in all the models considered here. The M estimations were 0.14 to 0.28 based on six models for chondrichthyans and osteichthyes. These values were similar to the six algorithms designed for cartilaginous fish, ranging from 0.16 to 0.35; for this reason, these mortality rates were considered low. The Z values ranged from 0.08 to 0.51; however, they were not considered given that the three estimations were less than M, and only the Z = 0.51 was considered. Given that Z = 0.51 and M = 0.24, an F = 0.27 was obtained by subtraction, indicating a low mortality by fishing. E had values between 0.21 and 0.53, which indicated overexploitation that exceeded the Eopt = 0.50 value. The obtained BRPs were Fopt = 0.10 and 0.12 and Flim = 0.16, which showed that the optimal fishing levels (best possible capture) to achieve long-term sustainable exploitation of the stock encompass 10 to 16% of the fishing effort applied for this species. However, the F surpassed this prudential range. The annual removal percentage (R = 21%) demonstrated that 21% of the population was being removed. Based on the biology and ecology of this species, all models applied in this study showed that P. kamoharai had low natural and fishing mortality rates and moderate total mortality; its exploitation rate exceeded the fishing limits. These values and their life history traits indicated that this shark species cannot tolerate any fishing level without threatening its populations. Full article
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Figure 1
<p>Geographic location of Santa Rosa of Salinas within the coastal profile of Continental Ecuador, Tropical Ecuadorian Pacific Ocean.</p>
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<p>Values of the instantaneous mortality rate (<span class="html-italic">M</span>) and annual natural mortality rate (<span class="html-italic">H<sub>M</sub></span>) of the crocodile shark, <span class="html-italic">Pseudocarcharias kamoharai</span>, in Pacific Ecuadorian waters estimated through diverse methods commonly used for osteichthyes and chondrichthyans. (1) Alverson and Carney [<a href="#B19-fishes-10-00005" class="html-bibr">19</a>], (2) Rickhter and Efanov [<a href="#B20-fishes-10-00005" class="html-bibr">20</a>], (3) Pauly [<a href="#B21-fishes-10-00005" class="html-bibr">21</a>], and (4–6) the three models by Jensen [<a href="#B22-fishes-10-00005" class="html-bibr">22</a>]. The dotted line represents the mean <span class="html-italic">M</span> and the continuous line the mean <span class="html-italic">H<sub>M</sub></span>.</p>
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<p>Estimations of the instantaneous rate of natural mortality (<span class="html-italic">M</span>) and the annual natural mortality rate (<span class="html-italic">H<sub>M</sub></span>) of the crocodile shark, <span class="html-italic">Pseudocarcharias kamoharai</span>, in Ecuadorian waters based on distinct models used specifically for cartilaginous fish. (1–2) Frisk et al. [<a href="#B23-fishes-10-00005" class="html-bibr">23</a>], (3–4) Then et al. [<a href="#B24-fishes-10-00005" class="html-bibr">24</a>], (5) Zhao et al. [<a href="#B25-fishes-10-00005" class="html-bibr">25</a>], and (6) from the literature related to sharks from the order Lamniformes. The yellow dotted line represents the mean <span class="html-italic">M</span>, and the blue continuous line is the mean <span class="html-italic">H<sub>M</sub></span>.</p>
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<p>The estimated exploitation rates of <span class="html-italic">Pseudocarcharias kamoharai</span> in Ecuadorian waters calculated using three methods. Methods employed were Beverton and Holt [<a href="#B33-fishes-10-00005" class="html-bibr">33</a>] and Cushing [<a href="#B34-fishes-10-00005" class="html-bibr">34</a>]. The dotted line represents the optimal exploitation rate according to the maximum sustainable yield. The continuous line is the average value obtained for the species.</p>
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<p>Size–frequency distribution of <span class="html-italic">Pseudocarcharias kamoharai</span> and its reference population parameters. The black vertical line represents the optimal capture average size according to the maximum sustainable yield (<span class="html-italic">L<sub>opt</sub></span>), the grey line is the sexual maturity size of the population, and the dotted line is the asymptotic length of the individuals.</p>
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21 pages, 55432 KiB  
Article
Significant Wave Height Retrieval in Tropical Cyclone Conditions Using CYGNSS Data
by Xiangyang Han, Xianwei Wang, Zhi He and Jinhua Wu
Remote Sens. 2024, 16(24), 4782; https://doi.org/10.3390/rs16244782 - 22 Dec 2024
Viewed by 469
Abstract
The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH [...] Read more.
The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3–6 m, the RMSE and bias were 0.94 m and −0.44 m, and for values above 6 m, they were 2.79 m and −2.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions. Full article
(This article belongs to the Special Issue Latest Advances and Application in the GNSS-R Field)
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Figure 1
<p>Distribution of the dataset (<b>a</b>), with the blue area indicating the coverage of CYGNSS data over the sea and the green area representing coastal seas, defined as regions 50 km away from the coastline. Histogram of SWH distribution for the overall dataset (<b>b</b>). Histogram of SWH distribution for the five typhoons in the testing dataset (<b>c</b>). ’TD’, ’TS’, ’STS’, ’TY’, ’STY’, and ’SuperTY’ denote tropical depression, tropical storm, severe tropical storm, tropical cyclone, severe typhoon, and super typhoon, respectively. The red box represents the area of statistics shown in (<b>c</b>).</p>
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<p>Daily data volume, maximum, minimum, and median values for the training, validation, and test datasets.</p>
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<p>The CYGNSS L1 delay-Doppler map (DDM) under three SWH conditions, (<b>a</b>) SWH = 2.48 m, (<b>b</b>) SWH = 4.11 m, (<b>c</b>) SWH = 6.01 m.</p>
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<p>The relationships between the feature variables and SWH. (<b>a</b>) DDMA, (<b>b</b>) LES, (<b>c</b>) DDM_peak.</p>
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<p>The main structure of this paper.</p>
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<p>The structure of the XGBoost-SC model.</p>
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<p>Comparison of different loss function where <math display="inline"><semantics> <mi>δ</mi> </semantics></math> in Huber loss is 0.3 and the <math display="inline"><semantics> <mi>δ</mi> </semantics></math> and h in S-Huber loss are 0.3 and 0.6, respectively.</p>
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<p>Scatter density plots of SWH between the ECMWF and the XGBoost-S and XGBoost-SC models in the overall testing dataset (<b>a</b>,<b>b</b>) and in the WNP during the five TCs from September to November in 2022 (<b>c</b>,<b>d</b>). The red line is the 1:1 line. (S represents S-Huber loss, and C represents the CDF matching).</p>
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<p>The histograms of SWHs by the ECMWF, XGBoost-S, and XGBoost-SC models in the overall testing dataset (<b>a</b>–<b>c</b>) and in the WNP during the five TCs from September to November in 2022 (<b>d</b>–<b>f</b>).</p>
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<p>The spatial distribution and histograms of SWHs by the ECMWF (<b>a</b>,<b>b</b>), BT (<b>c</b>,<b>d</b>), XGBoost-S (<b>e</b>,<b>f</b>), and XGBoost-SC (<b>g</b>,<b>h</b>) on 24 December 2022.</p>
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<p>Time series of daily RMSE and bias for SWH retrievals above 3 m by BT, XGBoost-S, and XGBoost-SC.</p>
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<p>Spatial distributions of SWHs by the ECMWF, BT, XGBoost-S, and XGBoost-SC models in the WNP during the five TCs of Hinnamnor on September 3 (<b>a</b>–<b>d</b>), Muifa on September 11 (<b>e</b>–<b>h</b>), Nanmadol on September 16 (<b>i</b>–<b>l</b>), Nesat on October 17 (<b>m</b>–<b>p</b>), and Nalgae on November 1 (<b>q</b>–<b>t</b>). The blue boxes represent the statistic area.</p>
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<p>The line chart of SWHs proportion by the ECMWF, BT, XGBoost-S, and XGBoost-SC models in the WNP during the five TCs of Hinnamnor (<b>a</b>), Muifa (<b>b</b>), Nanmadol (<b>c</b>), Nesat (<b>d</b>), and Nalgae (<b>e</b>).</p>
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<p>Spatial distributions of SWHs by the ECMWF, BT, XGBoost-S, and XGBoost-SC models during Hurricane Earl on September 8 (<b>a</b>–<b>d</b>), Fiona on September 22 (<b>e</b>–<b>h</b>), and Nicole on November 9 (<b>i</b>–<b>l</b>) and Severe Tropical Cyclone Darian on December 23 (<b>m</b>–<b>p</b>). The blue boxes represent the statistic area.</p>
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<p>The line chart of SWHs proportionally retrieved by the ECMWF, BT, XGBoost-S, and XGBoost-SC models during Earl (<b>a</b>), Fiona (<b>b</b>), Nicole (<b>c</b>), and Darian (<b>d</b>).</p>
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25 pages, 22457 KiB  
Article
Three-Dimensional Structural Analysis of Sea Temperature During Typhoon Transit
by Lingxiang Yao, Yanzhao Fu, Tao Wu, Junru Guo and Fei Shi
Water 2024, 16(24), 3641; https://doi.org/10.3390/w16243641 - 18 Dec 2024
Viewed by 618
Abstract
This study uses the Finite-Volume Community Ocean Model (FVCOM) to simulate the hydrodynamic processes during typhoon “Saola”. The simulation results closely match observed data. Typhoon “Saola” was a major system in the Pacific typhoon season, highlighting the complexity and uncertainty of tropical cyclone [...] Read more.
This study uses the Finite-Volume Community Ocean Model (FVCOM) to simulate the hydrodynamic processes during typhoon “Saola”. The simulation results closely match observed data. Typhoon “Saola” was a major system in the Pacific typhoon season, highlighting the complexity and uncertainty of tropical cyclone dynamics. By analyzing historical sea surface temperature data and the typhoon’s trajectory, the three-dimensional response of sea temperature during typhoon “Saola” was explored. The key findings are as follows: 1. Typhoon passage affects both coastal and deep-sea warming and cooling. Temperature changes are more pronounced near the coast, with the highest warming and cooling occurring within five days after the typhoon. In deep-sea areas, the highest warming occurs within five days, while the lowest cooling occurs within two days. 2. The nearshore water layers respond quickly to the typhoon, while the deep-sea water layers primarily respond in the middle depths, with a delayed effect. 3. In coastal shallow waters, the response is intense, with the maximum temperature increase and decrease occurring near the bottom, reaching 5.26 °C and −5.17 °C, respectively. In deep-sea areas, the response is weaker, with the maximum temperature change occurring near the surface: an increase of 0.49 °C and a decrease of −0.98 °C. The deepest response in coastal waters reaches about 80 m, while in the deep-sea area, it only reaches 50 m due to the thicker mixed layer. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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Figure 1
<p>Observation stations and the study area of typhoon “Saola”.</p>
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<p>Grid and Depth.</p>
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<p>Comparison chart of tidal flow and SST verification.</p>
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<p>Comparison chart of tidal flow and SST verification.</p>
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<p>Euler residual flow field during the passage of typhoon “Saola”.</p>
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<p>Temperature changes in the 7-day temperature of typhoon “Saola” ((<b>a</b>) typhoon transit for two days (48 h), (<b>b</b>) typhoon transit for two to five days (49 to 120 h), (<b>c</b>) typhoon transit for five to seven days (121 to 168 h)).</p>
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<p>Temperature changes in the 7-day temperature of typhoon “Saola” ((<b>a</b>) typhoon transit for two days (48 h), (<b>b</b>) typhoon transit for two to five days (49 to 120 h), (<b>c</b>) typhoon transit for five to seven days (121 to 168 h)).</p>
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<p>Current changes in the 7-day temperature of typhoon “Saola” ((<b>a</b>) typhoon transit for two days (48 h), (<b>b</b>) typhoon transit for two to five days (49 to 120 h), (<b>c</b>) typhoon transit for five to seven days (121 to 168 h)).</p>
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<p>Current changes in the 7-day temperature of typhoon “Saola” ((<b>a</b>) typhoon transit for two days (48 h), (<b>b</b>) typhoon transit for two to five days (49 to 120 h), (<b>c</b>) typhoon transit for five to seven days (121 to 168 h)).</p>
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<p>Average profile temperature for three days before transit ((<b>a</b>) is the nearshore area and (<b>b</b>) is the deep-sea area).</p>
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<p>Average profile temperature for three days before transit ((<b>a</b>) is the nearshore area and (<b>b</b>) is the deep-sea area).</p>
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<p>Temperature changes in the profile of the nearshore region (Region A) and the deep-sea region (Region B) within seven days of the typhoon’s transit: (<b>a</b>) 48 h of typhoon’s transit in the nearshore region; (<b>b</b>) 48 h of the typhoon’s transit in the deep-sea region; (<b>c</b>) 49 h~120 h of the typhoon’s transit in the nearshore region; (<b>d</b>) 49 h~120 h of the typhoon’s transit in the deep-sea region; (<b>e</b>) 121 h~168 h of the typhoon’s transit in the nearshore region; (<b>f</b>) 121 h~168 h of deep-sea area typhoon transit.</p>
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<p>Temperature changes in the profile of the nearshore region (Region A) and the deep-sea region (Region B) within seven days of the typhoon’s transit: (<b>a</b>) 48 h of typhoon’s transit in the nearshore region; (<b>b</b>) 48 h of the typhoon’s transit in the deep-sea region; (<b>c</b>) 49 h~120 h of the typhoon’s transit in the nearshore region; (<b>d</b>) 49 h~120 h of the typhoon’s transit in the deep-sea region; (<b>e</b>) 121 h~168 h of the typhoon’s transit in the nearshore region; (<b>f</b>) 121 h~168 h of deep-sea area typhoon transit.</p>
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<p>The water depth stratification of the regional model (where (<b>a</b>) is a coastal area (Region A) and (<b>b</b>) is a coastal area (Region A)).</p>
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<p>Model water layer temperature time variation diagram (where (<b>a</b>) is a shallow water area (Region A) and (<b>b</b>) is a deep-sea area (Region B)).</p>
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<p>Simulation of the surface temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after a typhoon passes: (<b>a</b>) represents the typhoon passing over the nearshore area after 48 h; (<b>b</b>) represents the typhoon passing over the deep-sea area after 48 h; (<b>c</b>) represents the typhoon passing over the nearshore area after 49 h to 120 h; (<b>d</b>) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (<b>e</b>) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (<b>f</b>) represents the typhoon passing over the deep-sea area after 121 h to 168 h).</p>
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<p>Simulation of the subsurface temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after a typhoon passes: (<b>a</b>) represents the typhoon passing over the nearshore area after 48 h; (<b>b</b>) represents the typhoon passing over the deep-sea area after 48 h; (<b>c</b>) represents the typhoon passing over the nearshore area after 49 h to 120 h; (<b>d</b>) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (<b>e</b>) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (<b>f</b>) represents the typhoon passing over the deep-sea area after 121 h to 168 h).</p>
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<p>Simulation of the third-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (<b>a</b>) represents the typhoon passing over the nearshore area after 48 h; (<b>b</b>) represents the typhoon passing over the deep-sea area after 48 h; (<b>c</b>) represents the typhoon passing over the nearshore area after 49 h to 120 h; (<b>d</b>) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (<b>e</b>) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (<b>f</b>) represents the typhoon passing over the deep-sea area after 121 h to 168 h).</p>
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<p>Simulation of the fourth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (<b>a</b>) represents the typhoon passing over the nearshore area after 48 h; (<b>b</b>) represents the typhoon passing over the deep-sea area after 48 h; (<b>c</b>) represents the typhoon passing over the nearshore area after 49 h to 120 h; (<b>d</b>) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (<b>e</b>) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (<b>f</b>) represents the typhoon passing over the deep-sea area after 121 h to 168 h).</p>
Full article ">Figure 14 Cont.
<p>Simulation of the fourth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (<b>a</b>) represents the typhoon passing over the nearshore area after 48 h; (<b>b</b>) represents the typhoon passing over the deep-sea area after 48 h; (<b>c</b>) represents the typhoon passing over the nearshore area after 49 h to 120 h; (<b>d</b>) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (<b>e</b>) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (<b>f</b>) represents the typhoon passing over the deep-sea area after 121 h to 168 h).</p>
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<p>Simulation of the fifth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (<b>a</b>) represents the typhoon passing over the nearshore area after 48 h; (<b>b</b>) represents the typhoon passing over the deep-sea area after 48 h; (<b>c</b>) represents the typhoon passing over the nearshore area after 49 h to 120 h; (<b>d</b>) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (<b>e</b>) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (<b>f</b>) represents the typhoon passing over the deep-sea area after 121 h to 168 h).</p>
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<p>Simulation of the fifth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (<b>a</b>) represents the typhoon passing over the nearshore area after 48 h; (<b>b</b>) represents the typhoon passing over the deep-sea area after 48 h; (<b>c</b>) represents the typhoon passing over the nearshore area after 49 h to 120 h; (<b>d</b>) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (<b>e</b>) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (<b>f</b>) represents the typhoon passing over the deep-sea area after 121 h to 168 h).</p>
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<p>Simulation of the sixth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (<b>a</b>) represents the typhoon passing over the nearshore area after 48 h; (<b>b</b>) represents the typhoon passing over the deep-sea area after 48 h; (<b>c</b>) represents the typhoon passing over the nearshore area after 49 h to 120 h; (<b>d</b>) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (<b>e</b>) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (<b>f</b>) represents the typhoon passing over the deep-sea area after 121 h to 168 h).</p>
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<p>The maximum and minimum of each layer from 48 h to 168 h (two to seven days) after the typhoon’s transit.</p>
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15 pages, 6823 KiB  
Technical Note
Investigating Tropical Cyclone Warm Core and Boundary Layer Structures with Constellation Observing System for Meteorology, Ionosphere, and Climate 2 Radio Occultation Data
by Xiaoxu Qi, Shengpeng Yang and Li He
Remote Sens. 2024, 16(22), 4257; https://doi.org/10.3390/rs16224257 - 15 Nov 2024
Viewed by 627
Abstract
The Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) collects data covering latitudes primarily between 40 degrees north and south, providing abundant data for tropical cyclone (TC) research. The radio occultation data provide valuable information on the boundary layer. However, quality [...] Read more.
The Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) collects data covering latitudes primarily between 40 degrees north and south, providing abundant data for tropical cyclone (TC) research. The radio occultation data provide valuable information on the boundary layer. However, quality control of the data within the boundary layer remains a challenging issue. The aim of this study is to obtain a more accurate COSMIC-2 radio occultation (RO) dataset through quality control (QC) and use this dataset to validate warm core structures and explore the planetary boundary layer (PBL) structures of TCs. In this study, COSMIC-2 data are used to analyze the distribution of the relative local spectral width (LSW) and the confidence parameter characterizing the random error of the bending angle. An LSW less than 20% is set as a data QC threshold, and the warm core and PBL composite structures of TCs at three intensities in the Northwest Pacific Ocean are investigated. We reproduce the warm core intensity and warm core height characteristics of TCs. In the radial direction of the typhoon eyewall, the impact height of the PBL increases from 3.45 km to 4 km, with the tropopause ranging from 160 hPa to 100 hPa. At the bottom of the troposphere, the variations in the positive and negative bias between the RO-detected and background field bending angles correspond well to the PBL heights, and the variations in the positive bias between the RO-detected and background field refractivity reach 14%. This research provides an effective QC method and reveals that the bending angle is sensitive to the PBL height. Full article
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Graphical abstract

Graphical abstract
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<p>Structure diagram of this study. This flowchart outlines the process from initial data processing to final outcome.</p>
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<p>Spatial distribution of the COSMIC-2 (<b>a</b>) local spectral width (10<sup>−3</sup> rad) and (<b>b</b>) confidence parameter (%) over the western Pacific Ocean from 2019 to 2022. The data include the TC intensities of TDs, TSs and TYs. The horizontal coordinate represents the distance from the RO profile to the center of the TC, and the vertical coordinate represents the impact height. The black dashed line in (<b>a</b>) shows the RO-observed lowest impact height.</p>
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<p>Spatial distribution of the COSMIC-2 relative local spectral width (%) over the western Pacific Ocean from 2019 to 2022. The data include the TC intensities of TDs, TSs, and TYs.</p>
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<p>Variation in the bending angle between the RO and ECMWF analyses when the LSW is &gt;35% (green curve), between 20% and 35% (blue curve), and &lt;20% (black curve).</p>
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<p>Distribution of LSWs after quality control. (<b>a</b>) Spatial distribution of LSWs (%) after quality control. (<b>b</b>) RO profile counts before quality control (shaded) and the proportion of counts after quality control (black curves).</p>
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<p>Mean (<b>a</b>) and standard deviation (<b>b</b>) of the variation in the bending angle between the RO values and ECMWF analyses before and after quality control.</p>
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<p>COSMIC-2 RO profile counts for the TD (red), TS (green), and TY (blue) categories derived from collocated RO data.</p>
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<p>Composite temperature (<math display="inline"><semantics> <mrow> <mi>T</mi> <mo>−</mo> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math>) cross-sections (shaded, °C), tropopause heights (black dashed lines), and boundary layer heights (black solid lines) for the TD (<b>a</b>), TS (<b>b</b>), and TY (<b>c</b>) categories.</p>
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<p>Composite spatial distribution of the bending angle between RO values and ECMWF analyses before (<b>a</b>–<b>c</b>) and after (<b>d</b>–<b>f</b>) quality control for the TD (<b>a</b>,<b>d</b>), TS (<b>b</b>,<b>e</b>), and TY (<b>c</b>,<b>f</b>) categories. The black lines represent the boundary layer heights.</p>
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<p>Composite spatial distributions of refractivity between RO values and ECMWF analyses before (<b>a</b>–<b>c</b>) and after (<b>d</b>–<b>f</b>) quality control for the TD (<b>a</b>,<b>d</b>), TS (<b>b</b>,<b>e</b>), and TY (<b>c</b>,<b>f</b>) categories. The black lines represent the boundary layer heights.</p>
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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 793
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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<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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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 894
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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<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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5 pages, 2215 KiB  
Interesting Images
The Box Crab Calappa hepatica as a Nuclear Species for the Opportunistic Foraging Behaviour of the Flowery Flounder, Bothus mancus, in the Indo-Pacific
by Federico Betti and Bert W. Hoeksema
Diversity 2024, 16(11), 662; https://doi.org/10.3390/d16110662 - 28 Oct 2024
Viewed by 819
Abstract
Some predatory fishes may exhibit opportunistic feeding behaviour by exploiting potential prey that is distracted, displaced, or exposed by the activities of a third party that acts as a ‘nuclear’ species. Other fishes mostly perform the role of ‘nuclear’ species, but benthic invertebrates, [...] Read more.
Some predatory fishes may exhibit opportunistic feeding behaviour by exploiting potential prey that is distracted, displaced, or exposed by the activities of a third party that acts as a ‘nuclear’ species. Other fishes mostly perform the role of ‘nuclear’ species, but benthic invertebrates, such as octopuses, have also been reported. Crabs are rarely observed in this role, with only a few records from the tropical Atlantic Ocean. Here, we report the temporary association between two specimens of the flowery flounder, Bothus mancus (family Bothidae), and a box crab, Calappa hepatica (family Calappidae), from the Philippines, representing the first record of a crab–fish feeding association in the Indo-Pacific region. Full article
(This article belongs to the Collection Interesting Images from the Sea)
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<p>Frontal view of the crab <span class="html-italic">Calappa hepatica</span> partially buried in the sand, with two <span class="html-italic">Bothus mancus</span> individuals waiting at short distances to opportunistically exploit potential prey disturbed by the crab’s movements and burying activities. Photo credit: F.B.</p>
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<p>(<b>a</b>) Map of the Philippines; (<b>b</b>) detail of the investigated area (white square and arrow indicate the ‘Coconut’ dive spot). Maps from Google Earth Pro 7.3.6.9796 (2024).</p>
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23 pages, 23516 KiB  
Article
Distribution and Seasonality of the Omura’s Whale (Balaenoptera omurai) in Australia Based on Passive Acoustic Recordings
by Ciara Edan Browne, Christine Erbe and Robert D. McCauley
Animals 2024, 14(20), 2944; https://doi.org/10.3390/ani14202944 - 12 Oct 2024
Viewed by 2205
Abstract
The Omura’s whale (Balaenoptera omurai) is one of the most recently described species of baleen whale. Initially known only from stranding and whaling specimens, it has now been identified in all ocean basins excluding the central and eastern Pacific. Unlike most [...] Read more.
The Omura’s whale (Balaenoptera omurai) is one of the most recently described species of baleen whale. Initially known only from stranding and whaling specimens, it has now been identified in all ocean basins excluding the central and eastern Pacific. Unlike most baleen whales that migrate between the poles and the equator seasonally, the Omura’s whale is known to inhabit tropical to sub-tropical waters year-round. In Australian waters, there remain fewer than 30 confirmed visual sightings over the past decade. However, based on acoustic records, the Omura’s whale has been detected off areas of the northwest coast of Australia year-round. This study utilises passive acoustic recordings from 41 locations around Australia from 2005 to 2023 to assess the distribution and seasonality of the Omura’s whale. The seasonal presence of Omura’s whale vocalisations varied by location, with higher presence at lower latitudes. Vocalisations were detected year-round in the Joseph Bonaparte Gulf in the Timor Sea, and near Browse Island and Scott Reef, in the Kimberley region. In the Pilbara region, acoustic presence mostly peaked from February to April and no acoustic presence was consistently observed from July to September across all sites. The most southerly occurrence of Omura’s whale vocalisations was recorded off the North West Cape in the Gascoyne region. Vocalisations similar but not identical to those of the Omura’s whale were detected in the Great Barrier Reef. The identified seasonal distribution provides valuable information to assess environmental and anthropogenic pressures on the Omura’s whale and to aid in creating management and conservation policies for the species in Australia. Full article
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<p>Deployment locations of USRs.</p>
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<p>Example spectrogram of five Omura’s whale vocalisations detected off northwest Australia. Spectrogram produced using a 512-point Hanning window with 1.172 Hz and 0.85 s frequency and time resolution, respectively; sampling frequency 600 Hz.</p>
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<p>(<b>a</b>) Spectrogram of a section of a recording sample with an Omura’s whale vocalisation present at 15–30 s, and (<b>b</b>) normalised amplitude envelope of the sample (blue curve) compared to the Omura’s whale vocalisation template (red curve).</p>
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<p>Bar plot of percentage of monthly vocal presence per recorded hours by site and year. Faded columns represent months with no data available. Sites with vocalisations in less than 15% of recordings were not included in this figure. JBG is the combined Bonaparte Gulf NE and SW sites. Scott Reef is the combined Scott Reef N and SE sites. CANPASS is the combined 1–7 sites in chronological order. Montebello is the combined Montebello, Montebello NW and NE sites. NWS W is the combined NWS W1 and W2 sites. NWS E is the combined NWS E1 and E2 sites. Ex. Plat. is the combined Exmouth Plateau and Exmouth Plateau W sites.</p>
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<p>Map of seasonal distribution of Omura’s whale vocal presence in the Kimberley and Timor Sea regions. Polar histograms represent the percentage of recorded hours with Omura’s whale vocal presence per month whereas the yellow segment represents January, and months follow in a clockwise direction to December in bright green. The segments in black represent months with no acoustic data available. White segments correspond to recordings without Omura’s detections. The red slashed circles represent sites with no Omura’s whale vocal presence found in available data.</p>
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<p>Map of seasonal distribution of Omura’s whale vocal presence in the Pilbara and Gascoyne regions. Polar histograms represent the percentage of recorded hours with Omura’s whale vocal presence per month, while the yellow segment represents January and months following in a clockwise direction to December in bright green. The segments in black represent months with no acoustic data available. White segments correspond to recordings without Omura’s detections. The red slashed circles represent sites with no Omura’s whale vocal presence found in available data.</p>
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<p>Sound spectrogram and waveform of potential Omura’s whale vocalisation detected off Lizard Island, Great Barrier Reef.</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 908
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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