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19 pages, 9233 KiB  
Article
Numerical Modeling on Ocean-Bottom Seismograph P-Wave Receiver Function to Analyze Influences of Seawater and Sedimentary Layers
by Wenfei Gong, Hao Hu, Aiguo Ruan, Xiongwei Niu, Wei Wang and Yong Tang
J. Mar. Sci. Eng. 2024, 12(11), 2053; https://doi.org/10.3390/jmse12112053 - 13 Nov 2024
Viewed by 447
Abstract
It is challenging to apply the receiver function method to teleseisms recorded by ocean-bottom seismographs (OBSs) due to a specific working environment that differs from land stations. Teleseismic incident waveforms reaching the area beneath stations are affected by multiple reflections generated by seawater [...] Read more.
It is challenging to apply the receiver function method to teleseisms recorded by ocean-bottom seismographs (OBSs) due to a specific working environment that differs from land stations. Teleseismic incident waveforms reaching the area beneath stations are affected by multiple reflections generated by seawater and sediments and noise resulting from currents. Furthermore, inadequate coupling between OBSs and the seabed basement and the poor fidelity of OBSs reduce the signal-to-noise ratio (SNR) of seismograms, leading to the poor quality of extracted receiver functions or even the wrong deconvolution results. For instance, the poor results cause strong ambiguities regarding the Moho depth. This study uses numerical modeling to analyze the influences of multiple reflections generated by seawater and sediments on H-kappa stacking and the neighborhood algorithm. Numerical modeling shows that seawater multiple reflections are mixed with the coda waves of the direct P-wave and slightly impact the extracted receiver functions and can thus be ignored in subsequent inversion processing. However, synthetic seismograms have strong responses to the sediments. Compared to the waveforms of horizontal and vertical components, the sedimentary responses are too strong to identify the converted waves clearly. The extracted receiver functions correspond to the above influences, resulting in divergent results of H-kappa stacking (i.e., the Moho depth and crustal average VP/VS ratio are unstable and have great uncertainties). Fortunately, waveform inversion approaches (e.g., the neighborhood algorithm) are available and valid for obtaining the S-wave velocity structure of the crust–upper mantle beneath the station, with sediments varying in thickness and velocity. Full article
(This article belongs to the Special Issue Modeling and Waveform Inversion of Marine Seismic Data)
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Figure 1

Figure 1
<p>Illustration of P- and S-wave receiver functions. Different colors correspond to different ray paths of multiple phases. (<b>a</b>) P-wave receiver function and the related ray paths; (<b>b</b>) the same as (<b>a</b>) but for S-wave receiver function.</p>
Full article ">Figure 2
<p>Normal oceanic crust models and their synthetic seismograms and RFs. (<b>a</b>) A layered velocity model for calculating synthetic seismograms and corresponding RFs. (<b>b</b>,<b>c</b>) Vertical and radial seismograms are calculated from different seawater thicknesses. (<b>d</b>) Synthetic RFs are computed from the waveforms shown in (<b>b</b>,<b>c</b>). The blue solid and dashed arrows represent positive and negative peaks caused by seawater multiple reflections.</p>
Full article ">Figure 3
<p>The same as <a href="#jmse-12-02053-f002" class="html-fig">Figure 2</a> but for models with a thick oceanic crust. (<b>a</b>) A layered velocity model for calculating synthetic seismograms and corresponding RFs. (<b>b</b>,<b>c</b>) Vertical and radial seismograms are calculated from different seawater thicknesses. (<b>d</b>) Synthetic RFs are computed from the waveforms shown in (<b>b</b>,<b>c</b>). The blue solid and dashed arrows represent positive and negative peaks caused by seawater multiple reflections.</p>
Full article ">Figure 4
<p>H-k stacking results of the normal oceanic crust models, where k is the average crustal V<sub>P</sub>/V<sub>S</sub> ratio; H<sub>M</sub> is the Moho depth; and H<sub>W</sub> is the thickness of the seawater layer; the blue dot represents the optimal value of Moho depth and the average crustal V<sub>P</sub>/V<sub>S</sub> ratio. (<b>a</b>) H-k stacking results of the normal oceanic crust model without a seawater layer. (<b>b</b>) The same as (<b>a</b>) but for a normal oceanic crust model with a 5 km thick seawater layer.</p>
Full article ">Figure 5
<p>The same as <a href="#jmse-12-02053-f004" class="html-fig">Figure 4</a> but for models with a thick oceanic crust, where k is the average crustal V<sub>P</sub>/V<sub>S</sub> ratio; H<sub>M</sub> is the Moho depth; and H<sub>W</sub> is the thickness of the seawater layer; the blue dot represents the optimal value of Moho depth and the average crustal V<sub>P</sub>/V<sub>S</sub> ratio. (<b>a</b>) H-k stacking results of the thick oceanic crust model without a seawater layer. (<b>b</b>) The same as (<b>a</b>) but for a thick oceanic crust model with a 5 km thick seawater layer.</p>
Full article ">Figure 6
<p>The same as <a href="#jmse-12-02053-f002" class="html-fig">Figure 2</a> but for models with a water layer (5 km), varying sedimentary layer thicknesses and a normal oceanic crust. (<b>a</b>) A layered velocity model for calculating synthetic seismograms and corresponding RFs. (<b>b</b>,<b>c</b>) Vertical and radial seismograms are calculated from different sedimentary layer thicknesses. (<b>d</b>) Synthetic RFs are computed from the waveforms shown in (<b>b</b>,<b>c</b>).</p>
Full article ">Figure 7
<p>The same as <a href="#jmse-12-02053-f002" class="html-fig">Figure 2</a> but for models with a seawater layer (5 km), varying sedimentary layers thicknesses and a thick oceanic crust. (<b>a</b>) A layered velocity model for calculating synthetic seismograms and corresponding RFs. (<b>b</b>,<b>c</b>) Vertical and radial seismograms are calculated from different sedimentary layer thicknesses. (<b>d</b>) Synthetic RFs are computed from the waveforms shown in (<b>b</b>,<b>c</b>).</p>
Full article ">Figure 8
<p>H-k stacking results of the normal oceanic crust models with a seawater layer (5 km) and sedimentary layers, where k represents the average crustal V<sub>P</sub>/V<sub>S</sub> ratio; H<sub>M</sub> represents the Moho depth; H<sub>C</sub> represents the presupposed oceanic crust thickness; and H<sub>S</sub> represents the submarine sedimentary layer thickness; the blue dot represents the optimal value of Moho depth and the average crustal V<sub>P</sub>/V<sub>S</sub> ratio. (<b>a</b>) The thickness of the sedimentary layer is 0.25 km. (<b>b</b>) The thickness of the sedimentary layer is 1.75 km.</p>
Full article ">Figure 9
<p>H-k stacking results of the thick oceanic crust models with a seawater layer (5 km) and sedimentary layers, where k represents the average crustal V<sub>P</sub>/V<sub>S</sub> ratio; H<sub>M</sub> represents the Moho depth; H<sub>C</sub> represents the presupposed oceanic crust thickness; and H<sub>S</sub> represents the submarine sedimentary layer thickness; the blue dot represents the optimal value of Moho depth and the average crustal V<sub>P</sub>/V<sub>S</sub> ratio. (<b>a</b>) The thickness of the sedimentary layer is 0.25 km. (<b>b</b>) The thickness of the sedimentary layer is 1.75 km.</p>
Full article ">Figure 10
<p>Comparison of the H-k stacking results between the presupposed and estimated values of the normal and thick oceanic crust model with a seawater thickness of 5 km and different sedimentary layer thicknesses. The presupposed crustal thickness excludes the sediment thickness. (<b>a</b>,<b>c</b>) The crustal thicknesses and their differences under models with the normal oceanic crust (6 km thick) and the thick oceanic crust (10 km thick), respectively; (<b>b</b>,<b>d</b>) the crustal V<sub>P</sub>/V<sub>S</sub> ratios and their differences under models with the normal oceanic crust (6 km thick) and the thick oceanic crust (10 km thick), respectively.</p>
Full article ">Figure 11
<p>Results of the NA inversion structures. In the synthetic tests, the thicknesses of the seawater and sediments are 5 km and 0.5 km, respectively. The V<sub>S</sub> of the sediments is 0.8 km/s. (<b>a</b>) The inversion results of the normal oceanic crust (6 km thick). The red and blue solid lines represent the best V<sub>S</sub> structure and V<sub>P</sub>/V<sub>S</sub> ratio; the orange dashed line indicates the Moho depth. (<b>b</b>) The same as (<b>a</b>) but for a model with a thick oceanic crust (10 km thick). (<b>c</b>) A comparison of the waveforms between the synthetic RFs (blue lines) and the RFs calculated from the inversion model (red lines). The solid lines correspond to the model with the normal oceanic crust, and the dashed lines correspond to the model with the thick oceanic crust.</p>
Full article ">Figure 12
<p>The same as <a href="#jmse-12-02053-f002" class="html-fig">Figure 2</a> but for models with a different Vs of the sedimentary layer. The oceanic crust, seawater and sedimentary layer thicknesses are 6 km, 5 km and 0.5 km, respectively. (<b>a</b>) A layered velocity model for calculating synthetic seismograms and corresponding RFs. (<b>b</b>,<b>c</b>) Vertical and radial seismograms are calculated from different sedimentary layer V<sub>S</sub>. (<b>d</b>) Synthetic RFs are computed from the waveforms shown in (<b>b</b>,<b>c</b>).</p>
Full article ">Figure 13
<p>The same as <a href="#jmse-12-02053-f003" class="html-fig">Figure 3</a> but for models with a different Vs for the sedimentary layer. The oceanic crust, seawater and sedimentary layer thicknesses are 10 km, 5 km and 0.5 km, respectively. (<b>a</b>) A layered velocity model for calculating synthetic seismograms and corresponding RFs. (<b>b</b>,<b>c</b>) Vertical and radial seismograms are calculated from different sedimentary layer V<sub>S</sub>. (<b>d</b>) Synthetic RFs are computed from the waveforms shown in (<b>b</b>,<b>c</b>).</p>
Full article ">Figure 14
<p>The same as <a href="#jmse-12-02053-f010" class="html-fig">Figure 10</a> but for models with a different Vs for the sedimentary layer. The seawater and sedimentary layer thicknesses are 5 km and 0.5 km, respectively. (<b>a</b>,<b>c</b>) The crustal thicknesses and their differences under models with the normal oceanic crust (6 km thick) and the thick oceanic crust (10 km thick), respectively; (<b>b</b>,<b>d</b>) the crustal V<sub>P</sub>/V<sub>S</sub> ratios and their differences under models with the normal oceanic crust (6 km thick) and the thick oceanic crust (10 km thick), respectively.</p>
Full article ">Figure 15
<p>The same as <a href="#jmse-12-02053-f011" class="html-fig">Figure 11</a> but for models with different properties. The seawater thickness and sedimentary layer thickness are 5 km and 0.5 km, respectively. The Vs of the sedimentary layer is 0.4 km/s. (<b>a</b>) The inversion results of the normal oceanic crust (6 km thick). The red and blue solid lines represent the best V<sub>S</sub> structure and V<sub>P</sub>/V<sub>S</sub> ratio; the orange dashed line indicates the Moho depth. (<b>b</b>) The same as (<b>a</b>) but for a model with a thick oceanic crust (10 km thick). (<b>c</b>) A comparison of the waveforms between the synthetic RFs (blue lines) and the RFs calculated from the inversion model (red lines). The solid lines correspond to the model with the normal oceanic crust, and the dashed lines correspond to the model with the thick oceanic crust.</p>
Full article ">
25 pages, 3719 KiB  
Article
Impact of Climate Change on Biodiversity and Implications for Nature-Based Solutions
by Cor A. Schipper, Titus W. Hielkema and Alexander Ziemba
Climate 2024, 12(11), 179; https://doi.org/10.3390/cli12110179 - 7 Nov 2024
Viewed by 1872
Abstract
The Intergovernmental Panel on Climate Change (IPCC) provides regular scientific assessments on climate change, its implications, and potential future risks based on estimated energy matrixes and policy pathways. The aim of this publication is to assess the risks climate change poses to biodiversity [...] Read more.
The Intergovernmental Panel on Climate Change (IPCC) provides regular scientific assessments on climate change, its implications, and potential future risks based on estimated energy matrixes and policy pathways. The aim of this publication is to assess the risks climate change poses to biodiversity using projected IPCC climate scenarios for the period 2081–2100, combined with key species-sensitivity indicators and variables as a response to climate change projections. In doing so, we address how climate-change-driven pressures may affect biodiversity. Additionally, a novel causal relationship between extreme ambient temperature exposure levels and the corresponding effects on individual species, noted in this paper as the Upper Thermal-Tolerance Limit and Species Sensitivity Distribution (UTTL-SSD), provides a compelling explanation of how global warming affects biodiversity. Our study indicates that North American and Oceanian sites with humid continental and subtropical climates, respectively, are poised to realize temperature shifts that have been identified as potential key tipping-point triggers. Heat stress may significantly affect approximately 60–90% of mammals, 50% of birds, and 50% of amphibians in North American and Oceanian sites for durations ranging from 5 to 84 days per year from 2080. In the humid temperate oceanic climate of European sites, the climate conditions remain relatively stable; however, moderate cumulative effects on biodiversity have been identified, and additional biodiversity-assemblage threat profiles exist to represent these. Both the integration of IPCC-IUCN profiles and the UTTL-SSD response relationship for the species communities considered have resulted in the identification of the projected threats that climate pressures may impose under the considered IPCC scenarios, which would result in biodiversity degradation. The UTTL-SSD responses developed can be used to highlight potential breakdowns among trophic levels in food web structures, highlighting an additional critical element when addressing biodiversity and ecosystem concerns. Full article
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Figure 1

Figure 1
<p>Schematization of the BBI model. The diagram outlines the flow of processed data from the IPCC climate change-induced variable on the IUCN biodiversity threat used to assess potential biodiversity degradation and places these in relation to local vulnerabilities to demonstrate effectiveness management plans in Köppen–Geiger climate zone projections for 2081–2100 [<a href="#B30-climate-12-00179" class="html-bibr">30</a>].</p>
Full article ">Figure 2
<p>Number of threatened species of abalones, amphibians, birds, conus, mammals, mollusks, and reptiles in NbS sites in Europe (<b>A</b>), North America (<b>B</b>), and Oceania (<b>C</b>) due to temperature extremes, storms and flooding, droughts, and habitat shifting and alteration. The total number of endemic species present in the IUCN database is given in parentheses.</p>
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<p>The Species Sensitivity Distributions for North America. The UTNZ and CTmax SSD–response relationship of invertebrate and vertebrate species is shown as sigmoid curves [<a href="#B40-climate-12-00179" class="html-bibr">40</a>]. The percentage of affected species at temperature (°C) is represented by the solid black line, with the model-averaged 95% confidence interval indicated by the shaded band, and the 5% affected species threshold by the black dotted line. In 2100 in North America, the ambient temperatures are projected to exceed 35 °C for 5–25 days (SSP1) and 33–85 days (SSP5) and to exceed 40 °C during 0–2 days (SSP1) and 5–22 days (SSP5). Note that large group of species show high sensitivity to the extreme temperatures projected in 2100.</p>
Full article ">Figure 4
<p>The Species Sensitivity Distributions for Europe. The UTNZ SSD–response relationship of vertebrate species is shown as sigmoid curves [<a href="#B40-climate-12-00179" class="html-bibr">40</a>]. The percentage of affected species at temperature (°C) is represented by the solid black line, with the model-averaged 95% confidence interval indicated by the shaded band, and the 5% affected species threshold by the black dotted line. In 2100, in Europe, the ambient temperatures are projected to exceed 35 °C for 0–1 days (SSP1) and 0–9 days (SSP5) and exceed 40 °C during 0 days (SSP1) and 0–2 days (SSP5). Note that while many species show high sensitivity to extreme temperatures, the projections show extreme temperature will be very uncommon in 2100.</p>
Full article ">Figure 5
<p>The Species Sensitivity Distributions for Oceania. The UTNZ and CT<sub>max</sub> SSD–response relationship of invertebrate and vertebrate species is shown as sigmoid curves [<a href="#B40-climate-12-00179" class="html-bibr">40</a>]. The percentage of affected species at temperature (°C) is represented by the solid black line, with the model-averaged 95% confidence interval indicated by the shaded band, and the 5% affected species threshold by the black dotted line. In 2100, in Oceanian sites, the ambient temperatures are projected to exceed 35 °C for 0 and 14 days (SSP1) and 0 and 41 days (SSP5), and to exceed 40 °C for 0 and 1 days (SSP1) and 0 and 7 days (SSP5). Note that large group of species show high sensitivity to extreme temperatures, which are projected to occur frequently only in East Australia in 2100.</p>
Full article ">Figure 6
<p>Visualization of food-web interactions at risk among key species groups in North America (<b>top</b>), Europe (<b>bottom left</b>), and Oceania (<b>bottom right</b>). Arrows show predator–prey interactions, with red arrows indicating prey is under pressure due to extreme temperatures. Thresholds result from extreme temperature IPCC projections for 2081–2100 under scenario SSP5-8.5 and UTTL-SSD response relationships. Species groups without UTTL-SSD data are shown in white.</p>
Full article ">Figure A1
<p>Overview of the NbS sites considered in this publication. The set consists of six coastal areas from Europe, Oceania, and North America, and six river catchments from Western Europe and North America.</p>
Full article ">Figure A2
<p>The Species Sensitivity Distributions for North America are presented below. The CTmax- and LT50-SSD response relationships for invertebrates, vertebrates, and plant species are shown as sigmoid curves [<a href="#B40-climate-12-00179" class="html-bibr">40</a>]. The percentage of affected species at temperature (°C) is represented by the solid black line, with the model-averaged 95% confidence interval indicated by the shaded band, and the 5% affected species threshold by the black dotted line. In 2100, in North America, the ambient temperatures are projected to exceed 35 °C for 5–25 days (SSP1) and 33–85 days (SSP5) and to exceed 40 °C for 0–2 days (SSP1) and 5–22 days (SSP5). Note that groups of species, such as reptiles and insects, show high sensitivity to the extreme temperatures projected in 2100.</p>
Full article ">Figure A3
<p>The Species Sensitivity Distributions for Europe are detailed below. The CTmax- and LT50-SSD response relationships for invertebrate species are shown as sigmoid curves [<a href="#B40-climate-12-00179" class="html-bibr">40</a>]. The percentage of affected species at temperature (°C) is represented by the solid black line, with the model-averaged 95% confidence interval indicated by the shaded band, and the 5% affected species threshold by the black dotted line. In 2100, in Europe, the ambient temperatures are projected to exceed 35 °C for 0–1 days (SSP1) and 0–9 days (SSP5), and nearly never exceed 40 °C for 0 days (SSP1) and 0–2 days (SSP5). Note that while many species show high sensitivity to extreme temperatures, the projections for extreme temperature will be very uncommon in 2100.</p>
Full article ">Figure A4
<p>The Species Sensitivity Distributions for Oceania are detailed below. The LT50-SSD–response relationships for fish species are shown as sigmoid curves [<a href="#B40-climate-12-00179" class="html-bibr">40</a>]. The percentage of affected species at temperature (°C) is represented by the solid black line, with the model-averaged 95% confidence interval indicated by the shaded band, and the 5% affected species threshold by the black dotted line. In 2100, in Oceanian sites, the ambient temperatures are projected to exceed 35 °C for 0 and 14 days (SSP1) and 0 and 41 days (SSP5), and to exceed 40 °C for 0 and 1 days (SSP1) and 0 and 7 days (SSP5).</p>
Full article ">
20 pages, 6853 KiB  
Article
Upper Ocean Thermodynamic Response to Coupling Currents to Wind Stress over the Gulf Stream
by Jackie May and Mark A. Bourassa
J. Mar. Sci. Eng. 2024, 12(11), 1994; https://doi.org/10.3390/jmse12111994 - 5 Nov 2024
Viewed by 542
Abstract
We use high-resolution coupled atmosphere–ocean model simulations over the Gulf Stream extension region during a winter season to examine the upper ocean thermodynamic response to including current feedback to atmospheric wind stress. We demonstrate that a model that includes current feedback leads to [...] Read more.
We use high-resolution coupled atmosphere–ocean model simulations over the Gulf Stream extension region during a winter season to examine the upper ocean thermodynamic response to including current feedback to atmospheric wind stress. We demonstrate that a model that includes current feedback leads to significant changes in the structure and transport of heat throughout the upper ocean in comparison to the same model without current feedback. We find that including the current feedback leads to changes in the upper ocean temperature pattern that match the vorticity pattern. Areas with cyclonic ocean vorticity, typically north of the Gulf Stream extension, correspond to areas with warmer temperatures throughout the water column. Areas with anticyclonic ocean vorticity, typically south of the Gulf Stream extension, correspond to areas with cooler temperatures throughout the water column. We also find that including current feedback leads to an overall reduction in the submesoscale vertical heat flux spectra across all spatial scales, with differences in the submesoscale vertical heat flux corresponding to SST minus mixed layer temperature differences. The direct impact of current feedback on the thermodynamic structure within the upper ocean also has indirect impacts on other aspects of the ocean, such as the energy transfer between the ocean and the atmosphere, ocean stratification, and acoustic parameters. Full article
(This article belongs to the Section Physical Oceanography)
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Figure 1

Figure 1
<p>COAMPS atmospheric model domains (purple) with 18 km, 6 km, and 2 km grid spacings and COAMPS ocean model domain (red) with 0.02° grid spacing. This is also <a href="#jmse-12-01994-f001" class="html-fig">Figure 1</a> in May and Bourassa [<a href="#B19-jmse-12-01994" class="html-bibr">19</a>].</p>
Full article ">Figure 2
<p>Wintertime seasonal (including December, January, and February) means over the Gulf Stream region for the a2o2 (<b>left</b>), a2o2-cfb (<b>middle</b>), and the difference between the a2o2-cfb and a2o2 (<b>right</b>) model simulations. The top row shows surface wind stress (shaded) and surface stress vectors (arrows), the middle row shows sea surface temperature (shaded) and vector ocean surface currents (arrows), and the bottom row shows sea surface salinity. The wintertime seasonal mean surface current &gt; 1.0 m s<sup>−1</sup> is contoured in white. The seasonal mean values are included in the top left of the panels. The differences in wind stress magnitude, SST, and salinity have very similar spatial patterns, although there are notable differences in each.</p>
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<p>Vertical cross-section of the wintertime seasonal mean ocean temperature (<b>top</b>) and ocean salinity (<b>bottom</b>) across the Gulf Stream extension at 72°W for the a2o2 (<b>left</b>), a2o2-cfb (<b>middle</b>), and the a2o2-cfb minus a2o2 (<b>right</b>) model simulation. The MLD is shown with the black line. Ocean vorticity (right only) is contoured at +/− 0.00001 s<sup>−1</sup>. The maximum current within the Gulf Stream extension along 72° W is depicted with the solid gray line. The viewpoint is from upstream, meaning north of the Gulf Stream extension is to the left (negative distance) and south of the Gulf Stream extension is to the right (positive distance) of the maximum current. The spatial patterns of salinity changes due to current feedback match patterns of temperature changes, larger scale relative vorticity, and the implied vertical motion due to this larger scale relative vorticity.</p>
Full article ">Figure 4
<p>Wintertime seasonal means of mixed layer depth over the Gulf Stream region for the a2o2 (<b>left</b>), a2o2-cfb (<b>middle</b>), and the difference between the a2o2-cfb and a2o2 (<b>right</b>) model simulations. The seasonal mean mixed layer depth is included in the top left of the panels.</p>
Full article ">Figure 5
<p>Vertical cross-section of the wintertime seasonal mean ocean density across the Gulf Stream extension at 72° W for the a2o2 (<b>left</b>), a2o2-cfb (<b>middle</b>), and the a2o2-cfb minus a2o2 (<b>right</b>) model simulation. The MLD is shown with the black lines. Ocean vorticity (right only) is contoured at +/− 0.00001 s<sup>−1</sup>. The maximum current within the Gulf Stream extension along 72° W is depicted with the solid gray line. The viewpoint is upstream, meaning north of the Gulf Stream extension is to the left (negative distance) and south of the Gulf Stream extension is to the right (positive distance) of the maximum current.</p>
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<p>As in <a href="#jmse-12-01994-f004" class="html-fig">Figure 4</a>, but for ocean heat content rate of change.</p>
Full article ">Figure 7
<p>Wintertime seasonal mean of the rate of change in mixed layer heat budget across the Gulf Stream extension at 72° W for the a2o2 (<b>top left</b>), a2o2-cfb (<b>top middle</b>), and the a2o2-cfb minus a2o2 (<b>top right</b>) model simulation; and for the mixed layer temperature gradient (<b>bottom left</b>) and surface current Laplacian (<b>bottom right</b>). The maximum current within the Gulf Stream extension along 72° W is depicted with the solid gray line. The viewpoint is upstream, meaning north of the Gulf Stream extension is to the left (negative distance) and south of the Gulf Stream extension is to the right (positive distance) of the maximum current.</p>
Full article ">Figure 8
<p>Six-day mean (15 to 20 January 2016) surface current (<b>top</b>) over the Gulf Stream region for the a2o2 (<b>left</b>) and a2o2-cfb (<b>right</b>) simulations. Mean surface current &gt; 1.0 m s<sup>−1</sup> is contoured in white. Select regions north (between 39° and 40° N and 71° to 72° W) and south (35.5° to 36.5° N and 72° to 73° W) of the Gulf Stream extension are shown with black boxes. PDFs of hourly mixed layer temperature advection (°C day<sup>−1</sup>) binned by mixed layer temperature gradient in mixed layer current direction (shown by the legend in units of °C m<sup>−1</sup>) from 15 to 20 January 2016 within the defined area north of the Gulf Stream extension (<b>middle</b>) and south of the Gulf Stream extension (<b>bottom</b>). Current feedback has a much greater impact on temperature gradients (in the current direction) on the north side of the Gulf Stream extension compared to the south side.</p>
Full article ">Figure 9
<p>As in <a href="#jmse-12-01994-f007" class="html-fig">Figure 7</a>, but for vertical entrainment.</p>
Full article ">Figure 10
<p>Wintertime seasonal mean of the SST minus the mixed layer temperature across the Gulf Stream extension at 72° W for the a2o2 (blue) and the a2o2-cfb (red) model simulations (<b>left</b>), and the difference between the two simulations (<b>right</b>). The maximum current within the Gulf Stream extension along 72° W is depicted with the solid gray line. The viewpoint is upstream, meaning north of the Gulf Stream extension is to the left (negative distance) and south of the Gulf Stream extension is to the right (positive distance) of the maximum current.</p>
Full article ">Figure 11
<p>As in <a href="#jmse-12-01994-f004" class="html-fig">Figure 4</a>, but for submesoscale vertical heat flux at 40 m depth.</p>
Full article ">Figure 12
<p>As in <a href="#jmse-12-01994-f005" class="html-fig">Figure 5</a>, but for submesoscale vertical heat flux.</p>
Full article ">Figure 13
<p>Power spectral density of submesoscale vertical heat flux at 40 m depth averaged over 15 days (1–15 January 2016) along 39° N (<b>top</b>) and 36° N (<b>bottom</b>) for the a2o2 and the a2o2-cfb model simulations.</p>
Full article ">
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 592
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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20 pages, 16568 KiB  
Article
Response of Upper Ocean to Parameterized Schemes of Wave Breaking under Typhoon Condition
by Xuhui Cao, Jie Chen, Jian Shi, Jingmin Xia, Wenjing Zhang, Zhenhui Yi, Hanshi Wang, Shaoze Zhang, Jialei Lv, Zeqi Zhao and Qianhui Wang
Remote Sens. 2024, 16(18), 3524; https://doi.org/10.3390/rs16183524 - 23 Sep 2024
Viewed by 531
Abstract
The study of upper ocean mixing processes, including their dynamics and thermodynamics, has been a primary focus for oceanographers and meteorologists. Wave breaking in deep water is believed to play a significant role in these processes, affecting air–sea interactions and contributing to the [...] Read more.
The study of upper ocean mixing processes, including their dynamics and thermodynamics, has been a primary focus for oceanographers and meteorologists. Wave breaking in deep water is believed to play a significant role in these processes, affecting air–sea interactions and contributing to the energy dissipation of surface waves. This, in turn, enhances the transfer of gas, heat, and mass at the ocean surface. In this paper, we use the FVCOM-SWAVE coupled wave and current model, which is based on the MY-2.5 turbulent closure model, to examine the response of upper ocean turbulent kinetic energy (TKE) and temperature to various wave breaking parametric schemes. We propose a new parametric scheme for wave breaking energy at the sea surface, which is based on the correlation between breaking wave parameter RB and whitecap coverage. The impact of this new wave breaking parametric scheme on the upper ocean under typhoon conditions is analyzed by comparing it with the original parametric scheme that is primarily influenced by wave age. The wave field simulated by SWAVE was verified using Jason-3 satellite altimeter data, confirming the effectiveness of the simulation. The simulation results for upper ocean temperature were also validated using OISST data and Argo float observational data. Our findings indicate that, under the influence of Typhoon Nanmadol, both parametric schemes can transfer the energy of sea surface wave breaking into the seawater. The new wave breaking parameter RB scheme effectively enhances turbulent mixing at the ocean surface, leading to a decrease in sea surface temperature (SST) and an increase in mixed layer depth (MLD). This further improves upon the issue of uneven mixing of seawater at the air–sea interface in the MY-2.5 turbulent closure model. However, it is important to note that wave breaking under typhoon conditions is only one aspect of wave impact on ocean disturbances. Therefore, further research is needed to fully understand the impact of waves on upper ocean mixing, including the consideration of other wave mechanisms. Full article
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<p>Location information of the Western Pacific region, water depth, typhoon track, and verification data: the illustrated area is the model range. (a–d) The four color lines are the paths of the Jason-3 satellite altimeter during the typhoon. (A1–A4) The red “+” indicates the location of the Argo buoy. The black “-o” line segment indicates the path of Typhoon Nanmadol, and the circle indicates the position of the typhoon every 6 h.</p>
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<p>The Western Pacific unstructured grid domain and model water depth information.</p>
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<p>Significant wave height (SWH) range affected by typhoon wind field; (<b>a</b>–<b>d</b>) stand for 0 o’clock every day from September 16 to 19; the blue “-o” line segment indicates the path of Typhoon Nanmadol, and the circle indicates the position of the typhoon every 6 h.</p>
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<p>Comparison of simulated significant wave height (SWH) with satellite altimeter data; (<b>a</b>–<b>d</b>) represent the four tracks passing through the typhoon area, corresponding to the four colored lines (a–d) in <a href="#remotesensing-16-03524-f001" class="html-fig">Figure 1</a>.</p>
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<p>The intensity and distribution of turbulent kinetic energy of the sea surface at 0 o’clock every day, and each row represents three different schemes, respectively; the yellow line indicates the path the typhoon has passed and its current location.</p>
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<p>Comparison of sea surface turbulent kinetic energy (TKE) simulation results between wave age scheme and broken wave parameter <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>B</mi> </msub> </mrow> </semantics></math> scheme; the red line indicates the path the typhoon has passed and its current location.</p>
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<p>The vertical turbulent energy of the ocean changes with the intensity of Typhoon Nanmadol (typhoon (33 m/s), strong typhoon (48 m/s), and super typhoon (62 m/s)). The green circle and straight line in (<b>a</b>–<b>c</b>) represent the TKE selected position and the direction of typhoon movement, respectively, and the curves in (<b>d</b>–<b>f</b>) represent the results of the three schemes, respectively.</p>
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<p>Sea surface temperature (SST) simulation results; (<b>a</b>–<b>d</b>) indicates the change of sea surface temperature at the current position of typhoon; the blue line indicates the path the typhoon has passed and its current location.</p>
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<p>Difference between daily SST and pre-typhoon SST, (<b>a</b>–<b>d</b>) represents the difference between the daily SST of the typhoon and that before the typhoon; the purple “-o” line segment indicates the path of Typhoon Nanmadol, and the circle indicates the position of the typhoon every 6 h.</p>
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<p>Comparison of simulated sea surface temperature between wave age parametric scheme and broken wave parameter <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>B</mi> </msub> </mrow> </semantics></math> scheme; the purple “-o” line segment indicates the path of Typhoon Nanmadol, and the circle indicates the position of the typhoon every 6 h.</p>
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<p>Comparison of sea surface temperature simulation results with OISST observation assimilation results; (<b>a</b>,<b>b</b>) represent the comparison between SST temperature and OISST temperature of wave age scheme and broken wave parameter scheme in typhoon region respectively.</p>
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<p>Simulation results of mixed layer temperature; (<b>a</b>,<b>b</b>) represent the cold pumping and heat pump effects of typhoon on the upper ocean, respectively; the dotted yellow line shows the boundary between the mixed layer and the thermocline.</p>
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<p>Comparison of vertical temperature simulation results with Argo floats’ data; (<b>a</b>–<b>d</b>) denotes four different Argo float locations.</p>
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18 pages, 20146 KiB  
Article
Changed Relationship between the Spring North Atlantic Tripole Sea Surface Temperature Anomalies and the Summer Meridional Shift of the Asian Westerly Jet
by Lin Chen, Gen Li and Jiaqi Duan
Atmosphere 2024, 15(8), 922; https://doi.org/10.3390/atmos15080922 - 1 Aug 2024
Viewed by 642
Abstract
The summer Asian westerly jet (AWJ)’s shifting in latitudes is one important characteristic of its variability and has great impact on the East Asian summer climate. Based on the observed and reanalyzed datasets from the Hadley Center Sea Ice and Sea Surface Temperature [...] Read more.
The summer Asian westerly jet (AWJ)’s shifting in latitudes is one important characteristic of its variability and has great impact on the East Asian summer climate. Based on the observed and reanalyzed datasets from the Hadley Center Sea Ice and Sea Surface Temperature dataset (HadISST), the Japanese 55-year reanalysis (JRA-55), and the fifth generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), this study investigates the relationship between the spring tripole North Atlantic SST (TNAT) anomalies and the summer meridional shift of the AWJ (MSJ) for the period of 1958–2020. Through the method of correlation analysis and regression analysis, we show that the ‘+ - +’ TNAT anomalies in spring could induce a northward shift of the AWJ in the following summer. However, such a climatic effect of the spring TNAT anomalies on the MSJ is unstable, exhibiting an evident interdecadal strengthening since the early 1990s. Further analysis reveals that this is related to a strengthened intensity of the spring TNAT anomalies in the most recent three decades. Compared to the early epoch (1958–1993), the stronger spring TNAT anomalies in the post epoch (1994–2020) could cause a stronger pan-tropical climate response until the following summer through a series of ocean–atmosphere interactions. Through Gill responses, the resultant more prominent cooling in the central Pacific in response to the ‘+ - +’ TNAT anomalies induces a pan-tropical cooling in the upper troposphere, which weakens the poleward gradient of the tropospheric temperature over subtropical Asia. As a result, the AWJ shifts northward via a thermal wind effect. By contrast, in the early epoch, the spring TNAT anomalies are relatively weaker, inducing weaker pan-tropical ocean–atmosphere interactions and thus less change in the meridional shit of the summer AWJ. Our results highlight a strengthened lagged effect of the spring TNAT anomalies on the following summer MSJ and have important implications for the seasonal climate predictability over Asia. Full article
(This article belongs to the Section Climatology)
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<p>(<b>a</b>) Climatology and (<b>b</b>) standard deviations of the 200 hPa zonal winds (m s<sup>−1</sup>) during boreal summer (June–July–August) for the period of 1958–2020. The solid black lines in (<b>a</b>,<b>b</b>) denote the axis of the Asian westerly jet (AWJ).</p>
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<p>(<b>a</b>) The leading mode of the empirical orthogonal function (EOF) analysis of the 200 hPa zonal wind anomalies over the region of 15° N–65° N, 30° E–180° E for the period of 1958–2020. (<b>b</b>) Time series of the first principal component (PC1) of the EOF analysis on the 200 hPa zonal wind anomalies over the region of 15° N–65° N, 30° E–180° E.</p>
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<p>Correlations of the summer meridional shift of the AWJ (MSJ) index with the spring sea surface temperature (SST) anomalies for the period of 1958–2020. The dots indicate the correlations at a significance level of <span class="html-italic">p</span> &lt; 0.1.</p>
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<p>Normalized time series of the spring tripole North Atlantic SST (TNAT) index (black solid line) and the summer MSJ index (black dashed line) for the period of 1958–2020. The solid blue line denotes their 21-year sliding correlation coefficients, with the dashed blue line denoting a significance level of <span class="html-italic">p</span> &lt; 0.05. The solid red line denotes the 15-year sliding correlation coefficients, with the dashed red line denoting a significance level of <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>(<b>a</b>) Correlations of the summer MSJ index with the spring SST anomalies for the period of 1958–1993. (<b>b</b>) Same as (<b>a</b>), but for the period of 1994–2020. The dots indicate the correlations at a significance level of <span class="html-italic">p</span> &lt; 0.1.</p>
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<p>The 21-year sliding standard deviation of the spring TNAT index.</p>
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<p>Regressions of (<b>a</b>) spring and (<b>c</b>) summer SST (shaded; °C), 500 hPa omega (contours; pa s<sup>−1</sup>; positive upward; the interval is 3 × 10<sup>−4</sup> pa s<sup>−1</sup> with the green/purple contours denoting negative/positive values) and 850 hPa wind anomalies (arrows; m s<sup>−1</sup>) onto the spring TNAT index for the period of 1958–1993. (<b>b</b>,<b>d</b>) Same as (<b>a</b>,<b>c</b>), but for the period of 1994–2020. The dots indicate the regressed SST anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. The black arrows indicate the zonal or meridional components of the wind anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. Wind speeds less than 0.25 m s<sup>−1</sup> are not shown. The absolute values of the omega anomalies less than 1.5 × 10<sup>−4</sup> pa s<sup>−1</sup> are not shown.</p>
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<p>Regressions of the summer 200 hPa velocity potential (shaded; 10<sup>5</sup> m<sup>2</sup> s<sup>−1</sup>), divergent wind (arrows; m s<sup>−1</sup>), and precipitation (contours; mm month<sup>−1</sup>; the interval is 7.5 mm month<sup>−1</sup> with the green/purple contours denoting negative/positive values) anomalies onto the spring TNAT index for the period of (<b>a</b>) 1958–1993 and (<b>b</b>) 1994–2020. Wind speeds less than 0.1 m s<sup>−1</sup> are not shown.</p>
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<p>(<b>a</b>) Regressions of the summer 200 hPa air temperature (shaded; K) and wind anomalies (arrows; m s<sup>−1</sup>) onto the spring TNAT index for the period of 1958–1993. (<b>b</b>) Same as (<b>a</b>), but for the period of 1994–2020. The dots indicate the regressed air temperature anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. The black arrows indicate the zonal or meridional components of the wind anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. Wind speeds less than 0.25 m s<sup>−1</sup> are not shown.</p>
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<p>The height–latitude cross-section of the summer climatological tropospheric temperature (contours; K) and its meridional gradient (shaded; 10<sup>−5</sup> K m<sup>−1</sup>) averaged between (<b>a</b>) 40° E–90° E, (<b>d</b>) 90° E–140° E. (<b>b</b>,<b>e</b>) same as (<b>a</b>,<b>d</b>), but for the summer anomalous tropospheric temperature (contours; K; the interval is 0.05 K) and its meridional gradient (shaded; 10<sup>−6</sup> K m<sup>−1</sup>) regressed onto the spring TNAT index for the period of 1958–1993. (<b>c</b>,<b>f</b>) same as (<b>b</b>,<b>e</b>), but for the period of 1994–2020. The absolute values of the tropospheric temperature anomalies less than 0.025 K are not shown.</p>
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<p>Regressed height–latitude cross-section of the summer zonal wind anomalies (shaded; m s<sup>−1</sup>) averaged between 40° E and 90° E onto the spring TNAT index for the period of (<b>a</b>) 1958–1993, (<b>b</b>) 1994–2020. The dots indicate the regressed zonal wind anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. The black solid lines are the zonal averaged (40° E–90° E) climatological zonal winds equal to or larger than 15 m s<sup>−1</sup> with the interval of 5 m s<sup>−1</sup>, denoting the westerly jet. (<b>c</b>,<b>d</b>) same as (<b>a</b>,<b>b</b>), but for those averaged between 90° E and 140° E.</p>
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<p>(<b>a</b>) Regressions of the summer 200 hPa zonal wind anomalies (m s<sup>−1</sup>) onto the spring TNAT index for the period of 1958–1993. (<b>b</b>) Same as (<b>a</b>), but for the period of 1994–2020. The dots indicate the regressed zonal wind anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. The black line denotes the AWJ axis.</p>
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<p>Spring TNAT index, summer pan-tropical SST index, summer poleward gradient of the 200 hPa tropospheric temperature (TT) index, and the summer MSJ index regressed onto the spring TNAT index for the periods of 1958–1993 (blue bars) and 1994–2020 (red bars). The solid bars indicate the regressed indices at a significance level of <span class="html-italic">p</span> &lt; 0.1. All data are standardized.</p>
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21 pages, 7948 KiB  
Article
Response of Sea Surface Temperature and Chlorophyll-a to Typhoon Lekima (2019)
by Yaowei Shi, Biyun Guo, Yuqian Niu, Venkata Subrahmanyam Mantravadi, Jushang Wang, Zhaokang Ji, Yingliang Che and Menglu Ye
Atmosphere 2024, 15(8), 919; https://doi.org/10.3390/atmos15080919 - 31 Jul 2024
Viewed by 660
Abstract
Typhoon (hurricane) is the most influential process of ocean–air interaction on the synoptic scale; it has a great influence on the heat exchange, mixing and ecological processes in the upper ocean, which in turn affect sea surface temperature (SST), leading to chlorophyll-a (Chl-a) [...] Read more.
Typhoon (hurricane) is the most influential process of ocean–air interaction on the synoptic scale; it has a great influence on the heat exchange, mixing and ecological processes in the upper ocean, which in turn affect sea surface temperature (SST), leading to chlorophyll-a (Chl-a) concentration variation. SST is also an important factor affecting marine fishery resources. Chl-a is closely related to the marine ecosystem and primary productivity. In this study, we analyzed the response of SST and Chl-a to Typhoon Lekima (2019) process. The result indicates that the response of temperature to typhoon decreases from the center to the outer edge, which has a good correlation with the location, path and influence area of the typhoon center. The mean SST in the study area (14°~40° N, 116°~136° E) decreased during the typhoon’s passage, from 28.97 °C at the beginning (5 August) to 28.22 °C (15 August). The concentration of Chl-a was high in the northwest and coastal areas; its mean value in the study area decreased from 2 to 8 August (on 2 and 8 August, the concentration was 0.484 mg/m3 and 0.405 mg/m3, respectively). From 8 to 14 August, Chl-a decreased with the increase in SST, and 10 and 14 August were the two peak values of Chl-a (while SST was low). Chl-a concentration increased after the typhoon’s landfall (from 15 to 31 August); the Chl-a trend was the same as that of SST. The stronger the typhoon and the longer the residence time, the greater the contribution to the increase in Chl-a concentration at sea surface. Typhoon-induced rainfall over the ocean surface, increased evaporation of seawater, enhanced mixing within the mixed layer and upwelling of the pycnocline resulted in an increase in Chl-a quantity. This study describes the spatial response of the upper ocean to typhoons. It provides a general method for the comprehensive assessment of typhoons in marginal seas and upper open oceans, which has wide applicability and good scientific application prospects. Full article
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<p>The route of Typhoon Lekima No. 9 in 2019.</p>
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<p>Wind direction and track of Typhoon Lekima. The blue line is the typhoon path; the red dot is the typhoon center; and the black arrow is the wind direction and speed (long arrows have higher wind speed and vice versa).</p>
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<p>Wind speed during Typhoon Lekima’s passage. Unit: m/s. The transition from blue to red indicates an increase in wind speed from low to high.</p>
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<p>The temperature changes around the track of the typhoon before and after its passage. The line from the southeast to the northwest is the path of Typhoon Lekima. White represents the track that the typhoon had passed, and red represents the track that the typhoon will pass. The black dot is the typhoon center.</p>
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<p>Changes in daily mean SST over the passage of Lekima from 1 to 31 August 2019.</p>
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<p>Spatial variation in EPV during Lekima’s passage (2019). The direction of the arrow represents the wind direction, and the length represents the wind speed. The background color ranges from blue to red, indicating a gradual increase in wind speed.</p>
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<p>Spatial and temporal distribution of sea surface Chl-a concentration during Lekima’s passage. Chl-a was converted to a logarithmic scale. Chl-a (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>l</mi> <mi>o</mi> <mi>g</mi> </mrow> <mrow> <mn>10</mn> </mrow> </msub> </mrow> </semantics></math> (mg/m<sup>3</sup>)). The line from the southeast to the northwest is the path of Typhoon Lekima. White represents the track that the typhoon had passed, and red represents the track that the typhoon will pass. The red dot is the typhoon center.</p>
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<p>Response of SST to Typhoon Lekima. The line from the southeast to the northwest is the path of Typhoon Lekima. Solid line represents the track that the typhoon had passed, and dotted line represents the track that the typhoon will pass. The blue dot is the typhoon center.</p>
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<p>Changes in daily mean SST over the passage of Lekima in Zhejiang coastal area from 1 to 31 August 2019.</p>
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<p>Chl-a concentration variation with SST during Lekima’s passage.</p>
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23 pages, 11064 KiB  
Article
Upper Ocean Responses to Tropical Cyclone Mekunu (2018) in the Arabian Sea
by Dan Ren, Shuzong Han and Shicheng Wang
J. Mar. Sci. Eng. 2024, 12(7), 1177; https://doi.org/10.3390/jmse12071177 - 13 Jul 2024
Viewed by 971
Abstract
Based on Argo observations and a coupled atmosphere–ocean–wave model, the upper ocean responses to the tropical cyclone (TC) Mekunu (2018) were investigated, and the role of a pre-existing cold eddy in modulating the temperature response to TC Mekunu was quantified by employing numerical [...] Read more.
Based on Argo observations and a coupled atmosphere–ocean–wave model, the upper ocean responses to the tropical cyclone (TC) Mekunu (2018) were investigated, and the role of a pre-existing cold eddy in modulating the temperature response to TC Mekunu was quantified by employing numerical experiments. With TC Mekunu’s passage, the mixed layer depth (MLD) on both sides of its track significantly deepened. Moreover, two cold patches (<26 °C) occurred, where the maximum cooling of the mixed layer temperature (MLT) reached 6.62 °C and 6.44 °C. Both the MLD and MLT changes exhibited a notable rightward bias. However, the changes in the mixed layer salinity (MLS) were more complex. At the early stage, the MLS on both sides of the track increased by approximately 0.5 psu. When TC Mekunu made landfall, the MLS change around the track was asymmetric. Significantly, a cold eddy pre-existed where the second cold patch emerged, and this eddy was intensified after TC Mekunu’s passage, with an average sea surface height reduction of approximately 2.7 cm. By employing the stand-alone ocean model, the numerical experimental results demonstrated that the pre-existing cold eddy enhanced TC-induced MLT cooling by an average of approximately 0.41 °C due to steeper temperature stratification at the base of mixed layer. Moreover, heat budget analysis indicated that the pre-existing cold eddy also enhanced subsurface temperature cooling mainly through zonal advection. Full article
(This article belongs to the Section Physical Oceanography)
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<p>Model domains for the WRF (black box) and ROMS (red box) models in the AS. The shading denotes the bathymetry.</p>
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<p>(<b>a</b>) COAWST model-simulated track (black dashed line) and IMD best track (black solid line) for TC Mekunu. The dots denote the positions of TC Mekunu center and their colors denote the intensity of TC Mekunu. The black pentagrams denote the positions of the Argo float (number 2901859) before and after the passage of TC Mekunu. The red solid line denotes the position of the S1 transect. This Argo float and S1 transect were used to analyze the subsurface response in <a href="#sec3dot2dot3-jmse-12-01177" class="html-sec">Section 3.2.3</a>. (<b>b</b>) Errors of the simulated locations of TC Mekunu center. The <span class="html-italic">x</span>-axis labels indicate the time in month/day/hour format (MM/DD/HH in UTC).</p>
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<p>Comparisons of the COAWST simulated (<b>a</b>) central air pressure and (<b>b</b>) maximum wind speed of TC Mekunu against those provided by the IMD and FNL data. The <span class="html-italic">x</span>-axis labels indicate the time in month/day/hour format (MM/DD/HH in UTC).</p>
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<p>Comparisons of the (<b>a</b>) temperature and (<b>b</b>) salinity between the COAWST simulations and Argo observations. The red solid lines denote the linear fit curves. The solid green lines denote the 95% confidence intervals.</p>
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<p>(<b>a</b>–<b>f</b>) Evolution of the sea surface height (SSH, shading) and sea surface currents (black arrows) before, during, and after TC Mekunu’s passage. The simulated track of TC Mekunu (black dashed lines) is overlaid, with the position and intensity depicted by colored dots. The black box in (<b>a</b>) marks the pre-existing cold eddy.</p>
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<p>(<b>a</b>–<b>f</b>) Evolution of the Ekman pumping velocity (EPV, shading) before, during, and after TC Mekunu’s passage. The black arrows indicate the 10 m wind vectors. The simulated track of TC Mekunu (black dashed lines) is overlaid, with the position and intensity depicted by colored dots.</p>
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<p>(<b>a</b>–<b>h</b>) Evolution of the mixed layer depth (MLD) before, during, and after TC Mekunu’s passage. (<b>i</b>) MLD differences at 00:00 UTC on 26 May with respect to the mean MLD on 20 May. The black solid lines denote the MLD contours. The simulated track of TC Mekunu (black dashed lines) is overlaid, with the position and intensity depicted by colored dots.</p>
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<p>(<b>a</b>–<b>h</b>) Evolution of the mixed layer temperature (MLT) before, during, and after TC Mekunu’s passage. (<b>i</b>) MLT differences at 00:00 UTC on 26 May with respect to the mean MLT on 20 May. The black solid lines denote the MLT contours. The simulated track of TC Mekunu (black dashed lines) is overlaid, with the position and intensity depicted by colored dots.</p>
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<p>(<b>a</b>–<b>h</b>) Evolution of the mixed layer salinity (MLS) before, during, and after TC Mekunu’s passage. (<b>i</b>) MLS differences at 00:00 UTC on 26 May with respect to the mean MLS on 20 May. The black solid lines denote the MLS contours. The simulated track of TC Mekunu (black dashed lines) is overlaid, with the position and intensity depicted by colored dots.</p>
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<p>Profiles of the (<b>a</b>) temperature, (<b>b</b>) salinity, and (<b>c</b>) density before (i.e., pre-Mekunu) and after (i.e., post-Mekunu) TC Mekunu’s passage. Changes in the (<b>d</b>) temperature, (<b>e</b>) salinity, and (<b>f</b>) density based on the Argo observations.</p>
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<p>Evolution of the temperature (shading) above 200 m along the transect (i.e., the red line in <a href="#jmse-12-01177-f002" class="html-fig">Figure 2</a>a) before, during, and after TC Mekunu’s passage. The black solid lines indicate the temperature contours. The green solid lines denote the MLD along the transect. The black dashed lines denote the longitudinal position of the TC Mekunu center at 06:00 UTC on 24 May.</p>
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<p>Distribution of the SSH, MLD, and D<sub>26</sub> on 20 May in (<b>a</b>–<b>c</b>) EXP1 and (<b>d</b>–<b>f</b>) EXP2. The track of TC Mekunu derived from the IMD (black dashed lines) is overlaid. The black boxes mark region C, which encompasses a pre-existing cold eddy.</p>
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<p>(<b>a</b>,<b>b</b>) MLD differences and (<b>c</b>,<b>d</b>) MLT differences at 00:00 UTC on 26 May with respect to the mean MLD/MLT on 20 May. The black solid lines denote the contours of the MLD/MLT differences. The track of TC Mekunu derived from the IMD (black dashed lines) is overlaid, with the position and intensity depicted by colored dots.</p>
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<p>(<b>a</b>) Temperature profiles on 20 May (i.e., pre-Mekunu) and at the time of maximum MLT cooling after TC Mekunu’s passage (i.e., post-Mekunu) at P1 in EXP1 and EXP2. (<b>b</b>) Temperature changes at P1 in EXP1 and EXP2.</p>
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<p>Heat budget analysis of the ocean above 200 m within region C in EXP1 and EXP2: (<b>a</b>,<b>b</b>) Local rate of temperature change, (<b>c</b>,<b>d</b>) vertical diffusion term, (<b>e</b>,<b>f</b>) total advection term (horizontal advection + vertical advection). The green solid lines denote the mean MLD in region C. The black dashed lines denote the moments when TC Mekunu entered and left region C. The <span class="html-italic">x</span>-axis labels indicate the time in month/day format (MM/DD in UTC).</p>
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<p>Mixed-layer heat budget analysis within region C in (<b>a</b>) EXP1 and (<b>b</b>) EXP2. Cumulative contributions of (<b>c</b>) vertical diffusion and (<b>d</b>) total advection to MLT change. The gray shading indicates the moment when the TC Mekunu center passed over region C. The <span class="html-italic">x</span>-axis labels indicate the time in month/day format (MM/DD in UTC).</p>
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<p>Cumulative contributions of zonal, meridional, and vertical advection to the temperature change in the 50–100 m layer. The gray shading indicates the moment when the TC Mekunu center passed over region C. The <span class="html-italic">x</span>-axis labels indicate the time in month/day format (MM/DD in UTC).</p>
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23 pages, 6958 KiB  
Article
Effect of Tropical Cyclone Wind Forcing on Improving Upper Ocean Simulation: An Idealized Study
by Xinxin Yue and Biao Zhang
Remote Sens. 2024, 16(14), 2574; https://doi.org/10.3390/rs16142574 - 13 Jul 2024
Viewed by 1018
Abstract
We examined how wind forcing affects the upper ocean response under idealized tropical cyclone (TC) conditions. In this study, we constructed three parameterized wind fields with varying spatial and temporal resolutions for TCs of different intensities and translation speeds. The simulated surface and [...] Read more.
We examined how wind forcing affects the upper ocean response under idealized tropical cyclone (TC) conditions. In this study, we constructed three parameterized wind fields with varying spatial and temporal resolutions for TCs of different intensities and translation speeds. The simulated surface and subsurface temperatures were cooler and deeper when using the blended wind fields owing to their higher wind speeds compared to those from coarse–resolution wind fields. Additionally, TC–induced currents were significantly stronger on the right side of the TC track, with notable differences in current velocities. Similar to the increase in ocean currents, the simulated turbulent kinetic energy driven by the blended winds is significantly higher than that simulated by the coarse-resolution wind fields. These findings suggest that using high-quality wind fields to drive ocean models can enhance the accuracy of the upper ocean response to TCs. The sensitivity of the upper ocean responses to wind forcing depends on the TC’s intensity and translation speed. Stronger and slower-moving TCs induce greater vertical shear and enhanced mixing. Therefore, accurate wind stress as a surface boundary condition is crucial for numerical ocean models. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) Domain configuration for all idealized experiments. The dashed line at 130°E represents the idealized tropical cyclone (TC) track. The red dot at the bottom represents the initial TC center, moving with a translation speed of 10 m/s. The blue arrow represents the propagation direction, while the red dot at the top represents the TC center at model time 72 h for the fast-moving TC. The light blue box represents the approximate area of impact for fast-moving TCs. Initial vertical profiles of (<b>b</b>) temperature, (<b>c</b>) salinity, and (<b>d</b>) density in the upper 200 m. The black dots represent the sigma levels used in the Regional Ocean Modeling System (ROMS).</p>
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<p>Spatial distribution of Category 3–5 Typhoons (referred to as STY) parameterized surface winds at model time 72 h for (<b>a</b>) the high-resolution TC parameterized asymmetric wind field (W1) with a spatial resolution of 1 km, (<b>b</b>) low-resolution TC parameterized asymmetric wind field (W2) with a spatial resolution of 25 km, and (<b>c</b>) blended winds (W3) between W1 and W2 with a spatial resolution of 8 km. RMW represents the radius of the maximum wind speed, TSP represents the translation speed, and MWS represents the maximum wind speed. (<b>d</b>) Parametric wind profiles showing the wind speed of W1. The black, blue, and red lines represent Tropical Storm (TS), Category 1–2 Typhoons (referred to as TY), and STY, respectively.</p>
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<p>Horizontal distributions of SST cooling (°C) at model time 72 h for (<b>a</b>) TS, (<b>b</b>) TY, and (<b>c</b>) STY driven by the W1 winds. Horizontal distributions of ocean currents (m/s) at model time 72 h for (<b>d</b>) TS, (<b>e</b>) TY, and (<b>f</b>) STY forced by the W1 winds. Horizontal distributions of turbulent kinetic energy (TKE) (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> <mo>/</mo> <mrow> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mrow> </mrow> </semantics></math>) at model time 72 h for (<b>g</b>) TS, (<b>h</b>) TY, and (<b>i</b>) STY forced by the W1 winds. The TC has a northward translation speed of 5 m/s, with RMW values of 66 km for TS, 35 km for TY, and 25 km for STY. The concentric dashed-line cycles represent the RMW, twice the RMW (2RMW), and four times the RMW (4RMW). The blue dashed lines indicate the TC track, and the red dots denote the TC center at model time 72 h.</p>
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<p>Vertical distributions of the subsurface temperature differences (°C) at model time 72 h for (<b>a</b>) TS, (<b>b</b>) TY, and (<b>c</b>) STY driven by the W1 winds. Vertical distributions of ocean currents (m/s) at model time 72 h for (<b>d</b>) TS, (<b>e</b>) TY, and (<b>f</b>) STY forced by the W1 winds. Vertical distributions of turbulent kinetic energy (TKE) (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> <mo>/</mo> <mrow> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mrow> </mrow> </semantics></math>) at model time 72 h for (<b>g</b>) TS, (<b>h</b>) TY, and (<b>i</b>) STY forced by the W1 winds. The TC has a northward translation speed of 5 m/s, with RMW values of 66 km for TS, 35 km for TY, and 25 km for STY. The black dotted lines mark the position of the TC center.</p>
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<p>Horizontal distributions of the simulated SST cooling difference (°C) with varying intensities of (<b>a</b>,<b>d</b>) TS, (<b>b</b>,<b>e</b>) TY, (<b>c</b>,<b>f</b>) STY. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The concentric dashed-line cycles indicate the RMW, 2RMW, and 4RMW. The blue dashed lines indicate the TC track, and the red dots indicate the TC center at model time 72 h.</p>
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<p>Vertical distributions of simulated temperature differences (°C) in the east–west direction for (<b>a</b>,<b>d</b>) TS, (<b>b</b>,<b>e</b>) TY, (<b>c</b>,<b>f</b>) STY. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The TC moves northward at 5 m/s. The black dotted lines mark the position of the TC center.</p>
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<p>Horizontal distributions of simulated near-surface current differences (m/s) with varying intensities of (<b>a</b>,<b>d</b>) TS, (<b>b</b>,<b>e</b>) TY, (<b>c</b>,<b>f</b>) STY. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The northward translation speed of the TC is 5 m/s. Concentric dashed-line circles indicate the RMW, 2RMW, and 4RMW. Blue dashed lines depict the TC track, and red dots indicate the TC center at model time 72 h.</p>
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<p>Vertical distributions of simulated ocean current differences (m/s) in the east–west direction with varying intensities of (<b>a</b>,<b>d</b>) TS, (<b>b</b>,<b>e</b>) TY, (<b>c</b>,<b>f</b>) STY. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The northward translation speed of the TC is 5 m/s. The black dotted lines mark the position of the TC center.</p>
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<p>Horizontal distributions of simulated SST cooling differences (°C) with varying translation speeds of (<b>a</b>,<b>d</b>) T3, (<b>b</b>,<b>e</b>) T5, (<b>c</b>,<b>f</b>) T10. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. Concentric dashed-line circles indicate RMW, 2RMW, and 4RMW. Blue dashed lines depict the TC track, and red dots indicate the TC center at model time 72 h.</p>
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<p>Vertical distributions of simulated temperature differences (°C) in the east–west direction for (<b>a</b>,<b>d</b>) T3, (<b>b</b>,<b>e</b>) T5, (<b>c</b>,<b>f</b>) T10. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The black dotted lines mark the position of the TC center.</p>
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<p>Horizontal distributions of simulated surface current differences (m/s) for (<b>a</b>,<b>d</b>) T3, (<b>b</b>,<b>e</b>) T5, (<b>c</b>,<b>f</b>) T10. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. Concentric dashed-line circles indicate RMW, 2RMW, and 4RMW. Blue dashed lines depict the TC track, and red dots indicate the TC center at model time 72 h.</p>
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<p>Vertical distributions of simulated eastward ocean current differences (m/s) in the east–west direction for (<b>a</b>,<b>d</b>) T3, (<b>b</b>,<b>e</b>) T5, (<b>c</b>,<b>f</b>) T10. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The black dotted lines mark the position of the TC center.</p>
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<p>The horizontal distributions of simulated TKE (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> <mo>/</mo> <mrow> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mrow> </mrow> </semantics></math>) between W1 and W2 winds for (<b>a1</b>) TS with a translation speed of 5 m/s, (<b>b1</b>) TY with a translation speed of 5 m/s, (<b>c1</b>) STY with a translation speed of 5 m/s, (<b>d1</b>) STY with a translation speed of 3 m/s, and (<b>e1</b>) STY with a translation speed of 10 m/s. The horizontal distributions of simulated TKE (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> <mo>/</mo> <mrow> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mrow> </mrow> </semantics></math>) between W1 and W3 winds for (<b>a2</b>) TS with a translation speed of 5 m/s, (<b>b2</b>) TY with a translation speed of 5 m/s, (<b>c2</b>) STY with a translation speed of 5 m/s, (<b>d2</b>) STY with a translation speed of 3 m/s, and (<b>e2</b>) STY with a translation speed of 10 m/s. The concentric dashed-line circles indicate the RMW, 2RMW, and 4RMW. The blue dashed lines represent the TC track, while the red dots mark the TC center at 72 h of model time.</p>
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20 pages, 12610 KiB  
Article
Influence of Radiation Stress on Upper-Layer Ocean Temperature under Geostrophic Condition
by Xuhui Cao, Jian Shi, Jie Chen, Qianhui Wang, Jialei Lv and Zeqi Zhao
Remote Sens. 2024, 16(13), 2288; https://doi.org/10.3390/rs16132288 - 22 Jun 2024
Viewed by 1010
Abstract
Wave-induced radiation stress (RS), as a primary driver of ocean currents influenced by waves, plays an important role in the response of upper ocean temperatures under typhoons. Previous studies have mainly focused on wave-generated currents and coastal currents in nearshore areas. This paper [...] Read more.
Wave-induced radiation stress (RS), as a primary driver of ocean currents influenced by waves, plays an important role in the response of upper ocean temperatures under typhoons. Previous studies have mainly focused on wave-generated currents and coastal currents in nearshore areas. This paper incorporates the geostrophic effect into the wave-induced radiation stress of wave-current interaction, and the effect of waves on the changes in upper ocean temperature (including sea surface temperature (SST) and mixed layer temperature) under typhoon Nanmadol (2022) is studied. The FVCOM-SWAVE model is used to conduct a preliminary numerical study in the western Pacific Ocean. The RS with the geostrophic effect increased the horizontal and vertical components, leading to an enhancement in turbulent mixing and a decrease in SST by up to 1.0 °C to 1.4 °C, which is closer to the SST obtained by OISST remote sensing fusion observation data. In the strong divergence domain, the direction of the vortex flow exhibits a more pronounced turn to the right, accompanied by an increase in water velocity. The vertical temperature profile of the ocean shows that the water below is perturbed by the RS component of the geostrophic effect, and the depth of the mixed layer increases by about 2 m, which is closer to the depth of the mixed layer observed by the Argo floats, indirectly enhancing the vertical mass transport of the ocean. In general, this shows that RS, which takes into account geostrophic effects, enhances the effect of waves on the water below, indirectly leading to lower temperatures in the upper ocean, and the simulated results align more closely with the observed data, offering valuable insights for enhancing marine numerical forecasting accuracy. Full article
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Figure 1

Figure 1
<p>Bathymetric display of the western Pacific domain and the location information of the data set are presented. The black “−o” line indicates the path of Typhoon Nanmadol, with the typhoon making landfall in Japan from the western Pacific Ocean; the circles along the path represent the position of the typhoon at 12 h intervals. The red “+” markers (A1−B4) denote the locations of the Argo floats; the straight line (a−d) illustrates the Jason-3 altimeter’s trajectory over the western Pacific during the typhoon.</p>
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<p>The western Pacific unstructured grid domain and model water depth information.</p>
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<p>Significant wave height (Hs) range affected by typhoon wind field; (<b>a</b>–<b>d</b>) stand for 0 o’clock every day from 15 to 18 September.</p>
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<p>The numerical simulation results of effective wave height were compared with the Jason-3 satellite altimeter data. (<b>a</b>–<b>d</b>) represent each of the four orbits of the altimeter satellite.</p>
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<p>Numerical simulation of the influence of Typhoon Nanmadol on ocean flow field. (<b>a</b>–<b>d</b>) stand for 0 o’clock every day from 15 to 18 September.</p>
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<p>Comparison of geostrophic effect radiation stress and conventional radiation stress simulated surface flow field vector images in typhoon center. (<b>a</b>,<b>b</b>) refers to the two moments when the typhoon became a super typhoon (48 m/s) and the maximum wind speed (62 m/s) respectively.</p>
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<p>Sea surface temperature simulation during Nanmadol by radiation stress under geostrophic effect. (<b>a</b>–<b>d</b>) stand for 0 o’clock every day from 15 to 18 September.</p>
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<p>Numerical simulation of the minimum difference in sea surface temperature between two schemes during Typhoon Nanmadol.</p>
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<p>Comparison of geostrophic effect RS-simulated SST with satellite fusion OISST data. (<b>a</b>–<b>d</b>) stand for 12 o’clock every day from 15 to 18 September.</p>
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<p>Comparison of observed sea water temperature and numerical simulation temperature profile with Argo floats; (<b>A1</b>–<b>B4</b>) indicate the locations of the Argo floats.</p>
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<p>Comparison of observed sea water temperature and numerical simulation temperature profile with Argo floats; (<b>A1</b>–<b>B4</b>) indicate the locations of the Argo floats.</p>
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20 pages, 26003 KiB  
Article
Impact of Satellite Wind on Improving Simulation of the Upper Ocean Response to Tropical Cyclones
by Xinxin Yue and Biao Zhang
Remote Sens. 2024, 16(11), 1832; https://doi.org/10.3390/rs16111832 - 21 May 2024
Cited by 1 | Viewed by 749
Abstract
Accurate modeling of the ocean response to tropical cyclones (TCs) requires high-quality wind fields to force ocean models. In this study, blended wind fields are generated using multi-source satellite data and the Climate Forecast System Reanalysis (CFSR) wind data. We utilize the hybrid [...] Read more.
Accurate modeling of the ocean response to tropical cyclones (TCs) requires high-quality wind fields to force ocean models. In this study, blended wind fields are generated using multi-source satellite data and the Climate Forecast System Reanalysis (CFSR) wind data. We utilize the hybrid wind fields to drive the Regional Ocean Modeling System (ROMS) for simulating oceanic dynamic and thermodynamic parameters. The model’s simulated ocean surface and sub-surface temperatures, as well as current speeds, are generally consistent with satellite and in situ observations collected during TC Winston and Freddy. The results are significantly better than those simulated by ROMS using wind forcing from CFSR alone. These results suggest that incorporating satellite wind data into the atmospheric forcing has the potential to enhance vertical mixing and improve simulations of the upper ocean response to TCs. Full article
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) Best track of Tropical Cyclones (TCs) Winston (2016) and Freddy (2023) from the International Best Track Archive for Climate Stewardship (IBTrACS). The color bar shows maximum sustained wind speed (m/s), which indicates typhoon intensity. (<b>b</b>,<b>d</b>) Translation Velocity (red), TC-Rossby Number (blue) for TC Winston (2016), and Freddy (2023) from IBTrACS. (<b>c</b>,<b>e</b>) Time series comparisons of maximum winds (black line) from IBTrACS, the Climate Forecast System Reanalysis (CFSR) winds (green line), and multiple-satellite winds (colorful dots) during TC Winston (2016) and Freddy (2023).</p>
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<p>(<b>a1</b>–<b>a4</b>) Spatial distribution of 10 m wind observed from the satellites at 18:50 UTC on 12 February, 21:06 UTC on 13 February, 05:35 UTC on 17 February, and 02:06 UTC on 21 February, 2016. (<b>b1</b>–<b>b4</b>) Spatial distribution of CFSR winds and (<b>c1</b>–<b>c4</b>) the blended winds at 19:00 UTC on 12 February, 21:00 UTC on 13 February, 06:00 UTC on 17 February, and 02:00 UTC on 21 February, 2016. (<b>d1</b>–<b>d4</b>) The difference between the blended winds and CFSR winds at Winston in 2016. The black line represents the TC track, and the colorful dots indicate the TC positions at 3 h intervals.</p>
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<p>(<b>a1</b>–<b>a4</b>) Spatial distribution of 10 m wind observed from the satellites at 17:57 UTC on 8 February, 11:17 UTC on 11 February, 12:18 UTC on 15 February, and 13:59 UTC on 18 February 2023. (<b>b1</b>–<b>b4</b>) The CFSR winds and (<b>c1</b>–<b>c4</b>) the blended winds at 18:00 UTC on 8 February, 11:00 UTC on 11 February, 12:00 UTC on 15 February, and 14:00 UTC on 18 February 2023. (<b>d1</b>–<b>d4</b>) The difference between the blended winds and CFSR winds at Freddy in 2023. The black line represents the TC track, and the colorful dots indicate the TC positions at 3 h intervals.</p>
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<p>The difference between the blended winds and the satellite winds for (<b>a</b>–<b>d</b>) TC Winston (2016) and (<b>e</b>–<b>h</b>) TC Freddy (2023). The black line represents the TC track, and the colorful dots indicate the TC positions at 3 h intervals.</p>
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<p>The Group for High-Resolution Sea Surface Temperature (GHRSST) data show SST changes induced by TC Winston: (<b>a</b>) on 12 February 2016; (<b>d</b>) on 17 February 2016; (<b>g</b>) on 21 February 2016. ROMS-simulated SST changes from CFSR winds: (<b>b</b>) on 12 February 2016; (<b>e</b>) on 17 February 2016; (<b>h</b>) on 21 February 2016. ROMS-simulated SST changes from the blended winds: (<b>c</b>) on 12 February 2016; (<b>f</b>) on 17 February 2016; (<b>i</b>) on 21 February 2016. The black line represents the TC track, and the colorful dots indicate the TC positions at 3 h intervals. The color bar represents the ocean temperature, in units of °C.</p>
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<p>The Group for High-Resolution Sea Surface Temperature (GHRSST) data show SST changes induced by TC Freddy: (<b>a</b>) on 8 February 2023; (<b>d</b>) on 11 February 2023; (<b>g</b>) on 18 February 2023. ROMS-simulated SST changes from CFSR winds: (<b>b</b>) on 8 February 2023; (<b>e</b>) on 11 February 2023; (<b>h</b>) on 18 February 2023. ROMS-simulated SST changes from the blended winds: (<b>c</b>) on 8 February 2023; (<b>f</b>) on 11 February 2023; (<b>i</b>) on 18 February 2023. The black line represents the TC track, and the colorful dots indicate the TC positions at 3 h intervals. The color bar represents the ocean temperature, in units of °C.</p>
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<p>The differences in wind speed between the blended winds and CFSR winds during TC Winston were as follows: (<b>a</b>) at 10:36 UTC on the 13; (<b>c</b>) at 12:40 UTC on the 16; (<b>e</b>) at 06:25 UTC on the 21 February 2016. The red star denotes the locations of Argo floats (R5902145, R5904145, and R5900953). The black line represents the TC track, and the colorful dots indicate the TCs positions at 3 h intervals. The color bar represents the wind speed in units of m/s. (<b>b</b>,<b>d</b>,<b>f</b>) The vertical temperature differences of the Argo measured (black line), ROMS simulated from CFSR winds (blue line), and the blended winds (red line) during the passage of TC Winston.</p>
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<p>(<b>a</b>) The difference in wind speed between the blended winds and CFSR winds during TC Freddy at 22:28 UTC on 10 February 2023. The red star denotes the location of the Argo float (R5905214). The black line represents the TC track, and the colorful dots indicate the TC positions at 3 h intervals. The color bar represents the wind speed in units of m/s. (<b>b</b>) The vertical temperature differences of the Argo measured (black line), ROMS simulated from CFSR winds (blue line), and the blended winds (red line).</p>
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<p>The satellite observed ocean surface wind speeds (first column) and the differences in the wind speed between CFSR and blended winds (second column) at 13:50 UTC on the 13, 02:06 UTC on the 21, and 06:25 UTC on the 21 February 2016. The simulated ocean surface current velocity differences between CFSR and the blended wind fields (third column) at 14:00 UTC on the 13, at 03:00 UTC on the 21, and at 06:00 UTC on 21 February 2016. The black line represents the TC track, and the colorful dots indicate the TC positions at 3 h intervals. The red star denotes the locations of surface drifter floats.</p>
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<p>Same as <a href="#remotesensing-16-01832-f009" class="html-fig">Figure 9</a>, but for TC Freddy (2023).</p>
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25 pages, 24325 KiB  
Article
The Investigation of the Response Mechanism of SST and Chlorophyll to Super Typhoon “Rey” in the South China Sea
by Shichao Wang, Jun Song, Junru Guo, Yanzhao Fu, Yu Cai and Linhui Wang
Water 2024, 16(4), 603; https://doi.org/10.3390/w16040603 - 18 Feb 2024
Viewed by 1450
Abstract
As one of the most significant disturbance sources in the upper marine environment of the South China Sea, tropical cyclones (typhoons) serve as a typical research subject for investigating the energy transfer process between the ocean and atmosphere. Utilizing satellite remote sensing data [...] Read more.
As one of the most significant disturbance sources in the upper marine environment of the South China Sea, tropical cyclones (typhoons) serve as a typical research subject for investigating the energy transfer process between the ocean and atmosphere. Utilizing satellite remote sensing data and focusing on Typhoon Rey No. 22’s transit event in 2021, this study quantitatively analyzes typhoon-induced energy input through heat pumping and cold suction at both surface and subsurface levels of the ocean. Additionally, it explores the response characteristics and feedback mechanisms of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) in the South China Sea to typhoon events. The research results show that the SST variation along the typhoon track displayed an asymmetric pattern, with a more pronounced warming on the right side and a cold anomaly lasting for 3–5 days on the left side. The subsurface warm anomaly dominated on the right side, showing a maximum temperature difference of 1.54 °C, whereas Ekman suction-induced upwelling led to cooling effects both at the subsurface and surface level on the left side, resulting in a maximum temperature difference of −3.28 °C. During the typhoon event, there was a significant decrease in sea surface heat flux, reaching 323.36 W/m2, accompanied by corresponding changes in SST due to processes such as upwelling, seawater mixing, and air–sea heat transfer dynamics where anomalies arising from oceanic dynamic processes and exchange through sea surface heat flux contributed equally. Furthermore, strong suction-induced upwelling during the typhoon influenced chlorophyll concentration within the central and western regions of the South China Sea (13.5° N–16.5° N, 111° E–112.5° E), resulting in significant enhancement and reaching its peak value at approximately 0.65 mg/L. The average chlorophyll concentration increased by approximately 0.31 mg/L. Full article
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<p>Chart of the track and intensity change of Typhoon Rey.</p>
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<p>The South China Sea is divided into eight research zones along the path of Typhoon Rey.</p>
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<p>Selected profile breakpoint location in South China Sea research area.</p>
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<p>Wind field and wind speed distribution during the passage of Typhoon Rey over the South China Sea (16–21 December 2021).</p>
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<p>Distribution map of regional wind curl over the South China Sea during Typhoon Rey.</p>
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<p>Ekman pumping intensity distribution map in the South China Sea research area.</p>
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<p>Net heat flux of the sea surface in the study area during Typhoon Rey.</p>
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<p>Net heat flux of the sea surface in the study area during Typhoon Rey.</p>
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<p>The sequence diagram of net heat flux change in the profile of the study area.</p>
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<p>SST distribution in the South China Sea research area during Typhoon Rey.</p>
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<p>SST distribution in the South China Sea research area during Typhoon Rey.</p>
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<p>SST variation distribution in the South China Sea during Typhoon Rey.</p>
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<p>SST variation distribution in the South China Sea during Typhoon Rey.</p>
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<p>The temperature curve of the R5, R6, and R7 zones with time was studied.</p>
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<p>R5, R6, and R7 Ekman pumping strength change line chart in the study area.</p>
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<p>The SST distribution profile varied with temperature in the study area.</p>
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<p>The SST distribution profile varied with temperature in the study area.</p>
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<p>Time variation distribution of the depth of the mixed layer.</p>
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<p>The variation of heat flux in the profile position led to the varied distribution of SST.</p>
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<p>Distribution of SST changes caused by upwelling.</p>
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<p>The distribution of sea surface temperature variation caused by the variation of mixed layer depth.</p>
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<p>Distribution of Chl-a concentration in the South China Sea during Typhoon Rey.</p>
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<p>Distribution of Chl-a concentration in the South China Sea during Typhoon Rey.</p>
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<p>Profile of Chl-a concentration in the South China Sea research area.</p>
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14 pages, 4438 KiB  
Technical Note
Typhoon-Induced Extreme Sea Surface Temperature Drops in the Western North Pacific and the Impact of Extra Cooling Due to Precipitation
by Jia-Yi Lin, Hua Ho, Zhe-Wen Zheng, Yung-Cheng Tseng and Da-Guang Lu
Remote Sens. 2024, 16(1), 205; https://doi.org/10.3390/rs16010205 - 4 Jan 2024
Viewed by 1753
Abstract
Sea surface temperature (SST) responses have been perceived as crucial to consequential tropical cyclone (TC) intensity development. In addition to regular cooling responses, a few TCs could cause extreme SST drops (ESSTDs) (e.g., SST drops more than 6 °C) during their passage. Given [...] Read more.
Sea surface temperature (SST) responses have been perceived as crucial to consequential tropical cyclone (TC) intensity development. In addition to regular cooling responses, a few TCs could cause extreme SST drops (ESSTDs) (e.g., SST drops more than 6 °C) during their passage. Given the extreme temperature differences and the consequentially marked air–sea flux modulations, ESSTDs are intuitively supposed to play a serious role in modifying TC intensities. Nevertheless, the relationship between ESSTDs and consequential storm intensity changes remains unclear. In this study, satellite-observed microwave SST drops and the International Best Track Archive for Climate Stewardship TC data from 2001 to 2021 were used to elucidate the relationship between ESSTDs and the consequential TC intensity changes in the Western North Pacific typhoon season (July–October). Subsequently, the distributed characteristics of ESSTDs were systematically examined based on statistical analyses. Among them, Typhoon Kilo (2015) triggered an unexpected ESSTD behind its passage, according to existing theories. Numerical experiments based on the Regional Ocean Modeling System were carried out to explore the possible mechanisms that resulted in the ESSTD due to Kilo. The results indicate that heavy rainfall leads to additional SST cooling through the enhanced sensible heat flux leaving the surface layer in addition to the cooling from momentum-driven vertical mixing. This process enhanced the sensible heat flux leaving the sea surface since the temperature of the raindrops could be much colder than the SST in the tropical ocean, specifically under heavy rainfall and relatively less momentum entering the upper ocean during Kilo. Full article
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<p>(<b>a</b>) Comparison of satellite-based OISST and available in situ SSTs measured via all surface drifters passing the study area from 2001 to 2021 (3,093,567 samples). (<b>b</b>) Comparison for the periods of TC passages regardless of the season (12,479 samples). (<b>c</b>,<b>d</b>) As in (<b>a</b>,<b>b</b>) but representing the bias between the satellite-based OISST and drifters (satellite–drifter). The color bar indicates the number of each bin.</p>
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<p>Number distribution of TICs with individual strengths corresponding to all TC passages in the WNP from 2001 to 2021. The box area outlines the TICs that belong to ESSTDs.</p>
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<p>Comparison of TICs (unit: °C) and TCI changes in delta wind speed (unit: ms<sup>−1</sup>). The blue dots show the means of each interval. The red bars show the 95% confidence interval, and the 25th and 75th percentiles are marked by black asterisks.</p>
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<p>Ratios of all TICs (including ESSTDs) corresponding to open ocean (<b>left</b>) and shelf regions (<b>right</b>).</p>
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<p>Distribution of all TICs in the shelf region (green dots) and open ocean (blue dots). Yellow and red dots denote ESSTDs that occurred in the shelf region and open ocean, respectively. ESSTDs that occurred in the open ocean (red dots) were triggered by the typhoons listed in <a href="#remotesensing-16-00205-t001" class="html-table">Table 1</a>.</p>
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<p>Sea surface cooling responses (relative to 4 September) during the passage of Kilo (2015) via (<b>a</b>) satellite-based OISSTs and (<b>b</b>) ROMS simulation. Color shades denote SSTs (left color bar) (unit: °C). TC intensities are marked with color dots (right color bar). Black dots denote the central positions of Kilo.</p>
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<p>Daily accumulated precipitation in different composites. <b>Climatology</b> is the domain (20–30°N, 160–170°E) average in September climatology. <b>Typhoon</b> is the average precipitation composite calculated from all TCs passing through the same domain from 2001 to 2020. <b>Kilo</b> is the average precipitation along the track of Kilo in the domain. The red line in the blue box denotes the median, and the lower and upper boundaries denote the 25th percentile and 75th percentile, respectively. Whiskers above and below the box indicate the 75th percentile + 1.5 × IQR and 25th percentile − 1.5 × IQR, respectively, where IQR denotes the 75th percentile–25th percentile. The red plus signs denote the outliers.</p>
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<p>Model-simulated sea surface cooling responses (relative to 4 September 2015) during the passage of Kilo (2015) with corrected term due to <span class="html-italic">Q<sub>p</sub></span> cooling. Color shades denote SSTs (left color bar) (unit: °C). TC intensities are marked with color dots (right color bar). Black dot denotes the central positions of Kilo.</p>
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17 pages, 9297 KiB  
Article
Manifestation of Internal Waves in the Structure of an Artificial Slick Band
by Alexey Ermoshkin, Olga Shomina, Aleksandr Molkov, Nikolay Bogatov, Mikhail Salin and Ivan Kapustin
Remote Sens. 2024, 16(1), 156; https://doi.org/10.3390/rs16010156 - 30 Dec 2023
Viewed by 1087
Abstract
The results of a field experiment devoted to observing slick-band shape variations occurring due to the action of heterogeneous currents related to the passage of internal waves are presented and analyzed on the basis of numerical simulation. The spatiotemporal structure of a train [...] Read more.
The results of a field experiment devoted to observing slick-band shape variations occurring due to the action of heterogeneous currents related to the passage of internal waves are presented and analyzed on the basis of numerical simulation. The spatiotemporal structure of a train of five solitons of internal waves has been retrieved. Their evolution in the coastal area is demonstrated based on the analysis of propagation characteristics. It is shown that the first soliton, characterized by the higher values of amplitude and width, collapsed when entering shallow water near the observation platform. The parameters of an artificial slick band affected by the passage of internal waves are determined. It is shown that the direction and width of the slick band are related to the direction and magnitude of the upper-ocean horizontal current, which contains a component related to the internal wave. The results of numerical simulation are qualitatively and quantitatively consistent with experimental data at short distances from the platform. An analysis of the conditions responsible for different regimes of slick-band response to the upper-ocean currents generated by propagating internal waves has been performed. Full article
(This article belongs to the Section Ocean Remote Sensing)
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<p>Extended slick bands in the areas of frequently appearing internal waves: Sentinel-1A, 27 September 2019, 04:52:36 UTC (<b>a</b>); Sentinel-1B, 8 January 2018, 17:33:15 UTC (<b>b</b>).</p>
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<p>Scheme of the experiment on 23 May 2019 (<b>a</b>), view of the platform (<b>b</b>) and photograph of the initial stage of the slick band formation (<b>c</b>). 1—X-band coherent radar station, 2—ultrasonic anemometer and weather station, 3—ADCP and 4—surfactant source. The circles correspond to distances from the radar station (grazing angles): 100 m (80.8°), 200 m (85.4°), 300 m (87°), 400 m (87.7°), and 500 m (88.2°). <span class="html-italic">u<sub>c</sub></span>, <span class="html-italic">u<sub>w</sub></span>, and IW denote the direction of the sea current, wind, and IW front.</p>
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<p>Acoustic backscattering for each of the four beams of ADCP during the experiment of 23 May 2019, 01:00–02:30 (UTC + 3). Beam 1—southward, beam 2—northward (under the platform), beam 3—westward, and beam 4—eastward.</p>
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<p>Vertical (<b>top</b>) and horizontal (<b>bottom</b>) velocity components and water temperature at 0.5 m depth (<b>bottom</b>) for the experiment of 23 May 2019.</p>
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<p>Radar image of the sea surface on May 23, 2019. RCS at 01:09 (<b>a</b>), 01:15 (<b>c</b>), 01:19 (<b>e</b>), 01:24 (<b>g</b>), 01:29 (<b>i</b>), and 01:33 (<b>k</b>); Doppler velocity at 01:09 (<b>b</b>), 01:15 (<b>d</b>), 01:19 (<b>f</b>), 01:24 (<b>h</b>), 01:29 (<b>j</b>), and 01:33 (<b>l</b>). The x axis is directed to the east and the y axis, to the north; (0,0) is the location of the radar and surfactant source.</p>
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<p>Radar image of the sea surface on May 23, 2019. RCS at 01:09 (<b>a</b>), 01:15 (<b>c</b>), 01:19 (<b>e</b>), 01:24 (<b>g</b>), 01:29 (<b>i</b>), and 01:33 (<b>k</b>); Doppler velocity at 01:09 (<b>b</b>), 01:15 (<b>d</b>), 01:19 (<b>f</b>), 01:24 (<b>h</b>), 01:29 (<b>j</b>), and 01:33 (<b>l</b>). The x axis is directed to the east and the y axis, to the north; (0,0) is the location of the radar and surfactant source.</p>
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<p>Spatial structure of the IW-related bands at 1:09 a.m. on 23 May 2019 (<b>a</b>); propagation velocities of individual solitons of the packet (<b>b</b>); amplitudes of the IW solitons, horizontal v and vertical w velocities in a layer of 0.5–12 m during the passage of the IW (<b>c</b>). The numbers show the depth values in meters. The asterisk shows the location of the radar on the platform.</p>
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<p>Speed and direction of the upper-ocean current (at a depth of 1 m) and near-surface wind during the experiment.</p>
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<p>Radar image of the sea surface during the passage of the IW2 soliton at 01:30 (<b>a</b>) and the scheme of the image processing (<b>b</b>): the solid line depicts the ASB boundaries, and the dotted line is the main direction.</p>
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<p>Comparison of the direction Q and width W of an artificial slick band with the direction <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math>, and speed <span class="html-italic">u<sub>d</sub></span> of the surface current during the passage of the IW solitons (<b>a</b>). Results of the corresponding correlation analysis (<b>b</b>). R is the correlation coefficient. Solid lines correspond to the linear approximation and the dotted lines to Q = <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Dynamics of the slick-band shape during the passage of a current disturbance related to the internal wave soliton. Simulation parameters correspond to the experiment (see the text). (<b>a</b>) t = 1000 s; (<b>b</b>) t = 4800 s. The color bar describes the disturbance modulus of the current speed. A semicircle marks the 100 m area.</p>
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<p>Possible “opposite” scenarios for the slick-band response to the passage of the internal wave soliton: (<b>a</b>) the slick-band trapping; (<b>b</b>) the “memory” effect.</p>
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16 pages, 7927 KiB  
Article
Tropical Cyclone-Induced Sea Surface Temperature Responses in the Northern Indian Ocean
by Jianmin Yu, Haibin Lv, Simei Tan and Yuntao Wang
J. Mar. Sci. Eng. 2023, 11(11), 2196; https://doi.org/10.3390/jmse11112196 - 18 Nov 2023
Cited by 1 | Viewed by 2389
Abstract
Tropical cyclones (TCs) exert a significant influence on the upper ocean, leading to sea surface temperature (SST) changes on a global scale. However, TC-induced SST responses exhibit considerable variability in the northern Indian Ocean (NIO), and the general understanding of these responses remains [...] Read more.
Tropical cyclones (TCs) exert a significant influence on the upper ocean, leading to sea surface temperature (SST) changes on a global scale. However, TC-induced SST responses exhibit considerable variability in the northern Indian Ocean (NIO), and the general understanding of these responses remains limited. This paper investigates the SST changes caused by 96 TCs over an 18-year period in the NIO. Through a composite analysis utilizing satellite SST data, a comprehensive study is conducted to examine the relationship between TC characteristics, including wind speed and translation speed, and the associated SST changes. The overall findings reveal that within a radius of 300 km from the TC center, SST decreases were observed at 1702 (86%) locations, with an average SST response to TC of −0.46 °C and a maximum decrease of −2.07 °C. The most significant reduction in SST typically occurred two days after the passage of TCs, followed by a gradual recovery period exceeding 15 days for the SSTs to return to their initial values. Consistent with findings in other ocean basins, stronger and slower-moving TCs induced more substantial cooling effects. Conversely, at 279 (14%) locations, particularly associated with TCs of weaker intensities, SST increases were observed following the TC passage. Notably, 140 of these locations were situated at low latitudes, specifically between 8° N and 15° N. This study provides a quantitative analysis of the comprehensive SST response to TCs in the NIO. Full article
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<p>(<b>a</b>) Averaged SST in the NIO along with 2500 m isobath (SL: Sri Lanka; SI: Sumatra Island); (<b>b</b>) TC tracks in the NIO overlaid on topography. The color of the TC points indicates the intensity category based on <a href="#jmse-11-02196-t001" class="html-table">Table 1</a>. The black and blue boxes delineate the highest densities of TCs in the Arabian Sea (10°–16° N and 62°–74° E) and Bay of Bengal (10°–16° N and 80°–92° E), respectively.</p>
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<p>(<b>a</b>) Monthly averaged SST and associated standard deviation in the Arabian Sea (black curve) and the Bay of Bengal (blue curve). (<b>b</b>) Monthly number of TC occurrences categorized by different intensity levels.</p>
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<p>The averaged wind speed (black bars) and translation speed (dark green bars) during different phases of TCs with (<b>a</b>) the average (bar) and standard deviation (vertical lines) during different phases of TCs. The phases are denoted by numbers, with 0 representing the beginning and 1 representing the end of a TC. The lifespan of TCs is divided into ten groups, and within each group, the average wind speed and translation speed are calculated. (<b>b</b>) Scatterplot of wind speed and translation speed for all TC locations. The color of the dots indicates the phases of the TCs.</p>
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<p>Spatial distribution of the frequency of areas impacted by TCs.</p>
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<p>Time series of the δSST (shading) for different TC locations. The locations are arranged in descending order based on the magnitude of TC-induced ΔSST; corresponding ΔSST is depicted by the black curve. The gray dashed line indicates the passage of the TC.</p>
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<p>(<b>a</b>) Tracks of TCs indicating their positions with positive ΔSST values. Scatterplots illustrate the relationship between the wind speeds of TCs and the corresponding climatological SSTs for locations with (<b>b</b>) positive ΔSST values and (<b>c</b>) other locations. In both scatterplots, colored points are used to indicate the season of occurrence, e.g., red represents autumn, blue represents winter, and green represents other seasons.</p>
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<p>Time series of averaged δSST and standard errors (vertical bars) before and after the passage of a TC.</p>
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<p>Bins of (<b>a</b>) wind speed and (<b>b</b>) translation speed of TCs and the corresponding average and standard error (vertical bars) of ΔSST. Linear regressions (red lines) were performed to determine the relationship between the features of TCs and bin-averaged ΔSST. The offset and slope of the regression line are labeled in each panel.</p>
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<p>Difference in pre-storm MLD between TC locations with (red box) positive and (blue box) negative ΔSST values for both (<b>left</b>) autumn and (<b>right</b>) winter seasons. The boxes represent the interquartile range (IQR), with the horizontal line inside each box indicating the median. The whiskers extend from the quartiles to another 1.5 times the IQR, unless they exceed the maximum or minimum values. The difference between the two categories for each season is significant at the 95% confidence level.</p>
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