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Particle Dynamics in the Nearshore of Lake Michigan Revealed by an Observation‐Modeling System
Given that few drifter experiments combined with a wave‐current coupled model system had been conducted in the complex nearshore area, this work was motivated to reveal the nearshore dynamics by applying an observation‐modeling system to Lake Michigan. Analysis of 11 surface drifters, wind, and curr...
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Published in: | Journal of geophysical research. Oceans 2020-08, Vol.125 (8), p.n/a |
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description | Given that few drifter experiments combined with a wave‐current coupled model system had been conducted in the complex nearshore area, this work was motivated to reveal the nearshore dynamics by applying an observation‐modeling system to Lake Michigan. Analysis of 11 surface drifters, wind, and current observations along the lake's eastern coast indicates that their trajectories are synergistically controlled by winds and initial releasing sites. Additionally, strong winds significantly impact nearshore dynamics, and the highly sensitive nearshore and offshore drifters are stranded in distinct regions. Simulations indicate that the model reproduces drifter trajectories and endpoints reasonably and that particle fates are mainly dominated by winds, while effects from heat flux and waves are also important. Further analysis of wave effects on particle dynamics indicates that both the wave‐induced sea surface roughness and Stokes drift advection are crucial to the simulated particle trajectories during wind events. Finally, virtual experiments confirm that particle dynamics are evidently susceptible to winds and initial locations. Overall, both the inclusion of physics effects (e.g., adding winds, heat fluxes, and waves) and diminishing the model uncertainties (e.g., from various wind data sources, wind drag coefficient formulations, model grids, and vertical turbulent mixing parameterizations) are important methods to improve the particle simulations. The successful application of this nearshore observation‐modeling system to Lake Michigan can be beneficial to the understanding of nearshore‐offshore transports and larval and fisheries recruitment success in similar freshwater and estuarine environments.
Plain Language Summary
We combine surface drifter observations and computer simulations to understand movements of particles in the nearshore of Lake Michigan. Analysis of drifters' moving paths, wind, and current measurements in the summers of 2014 and 2015 indicates that drifter movements are strongly related to winds and initial releasing sites. Multiple computer programs were run to simulate the designed scenarios by artificially removing one of the influencing factors at each simulation from the model. We find that particle movements are collectively affected by winds, heat flux transfer between the air and lake water, and surface gravity waves. Further investigations demonstrate that both wave‐induced sea surface roughness and Stokes drift advection are |
doi_str_mv | 10.1029/2019JC015765 |
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Plain Language Summary
We combine surface drifter observations and computer simulations to understand movements of particles in the nearshore of Lake Michigan. Analysis of drifters' moving paths, wind, and current measurements in the summers of 2014 and 2015 indicates that drifter movements are strongly related to winds and initial releasing sites. Multiple computer programs were run to simulate the designed scenarios by artificially removing one of the influencing factors at each simulation from the model. We find that particle movements are collectively affected by winds, heat flux transfer between the air and lake water, and surface gravity waves. Further investigations demonstrate that both wave‐induced sea surface roughness and Stokes drift advection are important to the simulated particle trajectories during wind events. After doing “what‐if” scenarios in the computer model, distinct particle routes driven by wind‐induced surface flows were simulated for virtual particles hypothetically released at various locations near the lake's southeastern coast. For example, nearshore group primarily shows longshore movements, while offshore one drifts into the deep basin. Overall, both the inclusion of physics effects (e.g., adding winds, heat fluxes, and waves) and diminishing the model uncertainties (e.g., from various wind data sources, wind drag coefficient formulations, model grids, and vertical turbulent mixing parameterizations) are important methods to improve the particle simulations. The outcome from this study will enhance the understanding of larval transport from nearshore to offshore areas in Lake Michigan and similar freshwater and estuarine environments.
Key Points
A wave‐current coupled particle model combined with surface drifter observations were applied to reveal nearshore particle dynamics
Winds dominate nearshore particle dynamics while effects of heat flux and waves are important as well
Including physical effects and diminishing uncertainties are important to the improved model performance</description><identifier>ISSN: 2169-9275</identifier><identifier>EISSN: 2169-9291</identifier><identifier>DOI: 10.1029/2019JC015765</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Advection ; Aerodynamics ; Brackishwater environment ; Computational fluid dynamics ; Computer programs ; Computer simulation ; Computer software ; Computers ; Current observations ; Data sources ; Drag ; Drag coefficient ; Drag coefficients ; Drift ; drifter observation ; Drifters ; Estuaries ; Estuarine environments ; Fisheries ; Formulations ; Freshwater ; Freshwater fish ; Freshwater lakes ; Geophysics ; Gravity waves ; Heat ; Heat flux ; Heat transfer ; Inland water environment ; Lakes ; Larvae ; Mathematical models ; Methods ; Modelling ; Nearshore dynamics ; numerical modeling ; Offshore ; Particle dynamics ; Particle trajectories ; Physics ; Sea surface ; Sea surface roughness ; Simulation ; Software ; Stokes drift ; Strong winds ; Surface drifters ; Surface gravity waves ; Surface roughness ; Trajectory control ; Turbulent mixing ; Uncertainty ; Wave analysis ; Wave effects ; waves ; Wind ; Wind effects ; Wind measurement ; Winds</subject><ispartof>Journal of geophysical research. Oceans, 2020-08, Vol.125 (8), p.n/a</ispartof><rights>2020. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a4340-9db754a768204a5600454e55374cd78dbc5ab1a72a54af4daee3d975cbbcffe13</citedby><cites>FETCH-LOGICAL-a4340-9db754a768204a5600454e55374cd78dbc5ab1a72a54af4daee3d975cbbcffe13</cites><orcidid>0000-0002-3013-4849 ; 0000-0002-4295-385X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Mao, Miaohua</creatorcontrib><creatorcontrib>Xia, Meng</creatorcontrib><title>Particle Dynamics in the Nearshore of Lake Michigan Revealed by an Observation‐Modeling System</title><title>Journal of geophysical research. Oceans</title><description>Given that few drifter experiments combined with a wave‐current coupled model system had been conducted in the complex nearshore area, this work was motivated to reveal the nearshore dynamics by applying an observation‐modeling system to Lake Michigan. Analysis of 11 surface drifters, wind, and current observations along the lake's eastern coast indicates that their trajectories are synergistically controlled by winds and initial releasing sites. Additionally, strong winds significantly impact nearshore dynamics, and the highly sensitive nearshore and offshore drifters are stranded in distinct regions. Simulations indicate that the model reproduces drifter trajectories and endpoints reasonably and that particle fates are mainly dominated by winds, while effects from heat flux and waves are also important. Further analysis of wave effects on particle dynamics indicates that both the wave‐induced sea surface roughness and Stokes drift advection are crucial to the simulated particle trajectories during wind events. Finally, virtual experiments confirm that particle dynamics are evidently susceptible to winds and initial locations. Overall, both the inclusion of physics effects (e.g., adding winds, heat fluxes, and waves) and diminishing the model uncertainties (e.g., from various wind data sources, wind drag coefficient formulations, model grids, and vertical turbulent mixing parameterizations) are important methods to improve the particle simulations. The successful application of this nearshore observation‐modeling system to Lake Michigan can be beneficial to the understanding of nearshore‐offshore transports and larval and fisheries recruitment success in similar freshwater and estuarine environments.
Plain Language Summary
We combine surface drifter observations and computer simulations to understand movements of particles in the nearshore of Lake Michigan. Analysis of drifters' moving paths, wind, and current measurements in the summers of 2014 and 2015 indicates that drifter movements are strongly related to winds and initial releasing sites. Multiple computer programs were run to simulate the designed scenarios by artificially removing one of the influencing factors at each simulation from the model. We find that particle movements are collectively affected by winds, heat flux transfer between the air and lake water, and surface gravity waves. Further investigations demonstrate that both wave‐induced sea surface roughness and Stokes drift advection are important to the simulated particle trajectories during wind events. After doing “what‐if” scenarios in the computer model, distinct particle routes driven by wind‐induced surface flows were simulated for virtual particles hypothetically released at various locations near the lake's southeastern coast. For example, nearshore group primarily shows longshore movements, while offshore one drifts into the deep basin. Overall, both the inclusion of physics effects (e.g., adding winds, heat fluxes, and waves) and diminishing the model uncertainties (e.g., from various wind data sources, wind drag coefficient formulations, model grids, and vertical turbulent mixing parameterizations) are important methods to improve the particle simulations. The outcome from this study will enhance the understanding of larval transport from nearshore to offshore areas in Lake Michigan and similar freshwater and estuarine environments.
Key Points
A wave‐current coupled particle model combined with surface drifter observations were applied to reveal nearshore particle dynamics
Winds dominate nearshore particle dynamics while effects of heat flux and waves are important as well
Including physical effects and diminishing uncertainties are important to the improved model performance</description><subject>Advection</subject><subject>Aerodynamics</subject><subject>Brackishwater environment</subject><subject>Computational fluid dynamics</subject><subject>Computer programs</subject><subject>Computer simulation</subject><subject>Computer software</subject><subject>Computers</subject><subject>Current observations</subject><subject>Data sources</subject><subject>Drag</subject><subject>Drag coefficient</subject><subject>Drag coefficients</subject><subject>Drift</subject><subject>drifter observation</subject><subject>Drifters</subject><subject>Estuaries</subject><subject>Estuarine environments</subject><subject>Fisheries</subject><subject>Formulations</subject><subject>Freshwater</subject><subject>Freshwater fish</subject><subject>Freshwater lakes</subject><subject>Geophysics</subject><subject>Gravity waves</subject><subject>Heat</subject><subject>Heat flux</subject><subject>Heat transfer</subject><subject>Inland water environment</subject><subject>Lakes</subject><subject>Larvae</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Modelling</subject><subject>Nearshore dynamics</subject><subject>numerical modeling</subject><subject>Offshore</subject><subject>Particle dynamics</subject><subject>Particle trajectories</subject><subject>Physics</subject><subject>Sea surface</subject><subject>Sea surface roughness</subject><subject>Simulation</subject><subject>Software</subject><subject>Stokes drift</subject><subject>Strong winds</subject><subject>Surface drifters</subject><subject>Surface gravity waves</subject><subject>Surface roughness</subject><subject>Trajectory control</subject><subject>Turbulent mixing</subject><subject>Uncertainty</subject><subject>Wave analysis</subject><subject>Wave effects</subject><subject>waves</subject><subject>Wind</subject><subject>Wind effects</subject><subject>Wind measurement</subject><subject>Winds</subject><issn>2169-9275</issn><issn>2169-9291</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp90M1Kw0AQB_AgCpbamw-w4NXofmazR4laLa2Vque42UzarWlSd9NKbj6Cz-iTGKmIJ-cyM_BjBv5BcEzwGcFUnVNM1CjBRMhI7AU9SiIVKqrI_u8sxWEw8H6Ju4pJzLnqBc_32jXWlIAu20qvrPHIVqhZALoD7fyidoDqAo31C6CJNQs71xWawRZ0CTnKWtSt08yD2-rG1tXn-8ekzqG01Rw9tL6B1VFwUOjSw-Cn94On66vH5CYcT4e3ycU41JxxHKo8k4JrGcUUcy0ijLngIAST3OQyzjMjdEa0pLpTBc81AMuVFCbLTFEAYf3gZHd37erXDfgmXdYbV3UvU8qZZIopFXfqdKeMq713UKRrZ1fatSnB6XeM6d8YO852_M2W0P5r09FwllBOCGZfLqJ0WQ</recordid><startdate>202008</startdate><enddate>202008</enddate><creator>Mao, Miaohua</creator><creator>Xia, Meng</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0002-3013-4849</orcidid><orcidid>https://orcid.org/0000-0002-4295-385X</orcidid></search><sort><creationdate>202008</creationdate><title>Particle Dynamics in the Nearshore of Lake Michigan Revealed by an Observation‐Modeling System</title><author>Mao, Miaohua ; Xia, Meng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a4340-9db754a768204a5600454e55374cd78dbc5ab1a72a54af4daee3d975cbbcffe13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Advection</topic><topic>Aerodynamics</topic><topic>Brackishwater environment</topic><topic>Computational fluid dynamics</topic><topic>Computer programs</topic><topic>Computer simulation</topic><topic>Computer software</topic><topic>Computers</topic><topic>Current observations</topic><topic>Data sources</topic><topic>Drag</topic><topic>Drag coefficient</topic><topic>Drag coefficients</topic><topic>Drift</topic><topic>drifter observation</topic><topic>Drifters</topic><topic>Estuaries</topic><topic>Estuarine environments</topic><topic>Fisheries</topic><topic>Formulations</topic><topic>Freshwater</topic><topic>Freshwater fish</topic><topic>Freshwater lakes</topic><topic>Geophysics</topic><topic>Gravity waves</topic><topic>Heat</topic><topic>Heat flux</topic><topic>Heat transfer</topic><topic>Inland water environment</topic><topic>Lakes</topic><topic>Larvae</topic><topic>Mathematical models</topic><topic>Methods</topic><topic>Modelling</topic><topic>Nearshore dynamics</topic><topic>numerical modeling</topic><topic>Offshore</topic><topic>Particle dynamics</topic><topic>Particle trajectories</topic><topic>Physics</topic><topic>Sea surface</topic><topic>Sea surface roughness</topic><topic>Simulation</topic><topic>Software</topic><topic>Stokes drift</topic><topic>Strong winds</topic><topic>Surface drifters</topic><topic>Surface gravity waves</topic><topic>Surface roughness</topic><topic>Trajectory control</topic><topic>Turbulent mixing</topic><topic>Uncertainty</topic><topic>Wave analysis</topic><topic>Wave effects</topic><topic>waves</topic><topic>Wind</topic><topic>Wind effects</topic><topic>Wind measurement</topic><topic>Winds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mao, Miaohua</creatorcontrib><creatorcontrib>Xia, Meng</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Journal of geophysical research. Oceans</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mao, Miaohua</au><au>Xia, Meng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Particle Dynamics in the Nearshore of Lake Michigan Revealed by an Observation‐Modeling System</atitle><jtitle>Journal of geophysical research. Oceans</jtitle><date>2020-08</date><risdate>2020</risdate><volume>125</volume><issue>8</issue><epage>n/a</epage><issn>2169-9275</issn><eissn>2169-9291</eissn><abstract>Given that few drifter experiments combined with a wave‐current coupled model system had been conducted in the complex nearshore area, this work was motivated to reveal the nearshore dynamics by applying an observation‐modeling system to Lake Michigan. Analysis of 11 surface drifters, wind, and current observations along the lake's eastern coast indicates that their trajectories are synergistically controlled by winds and initial releasing sites. Additionally, strong winds significantly impact nearshore dynamics, and the highly sensitive nearshore and offshore drifters are stranded in distinct regions. Simulations indicate that the model reproduces drifter trajectories and endpoints reasonably and that particle fates are mainly dominated by winds, while effects from heat flux and waves are also important. Further analysis of wave effects on particle dynamics indicates that both the wave‐induced sea surface roughness and Stokes drift advection are crucial to the simulated particle trajectories during wind events. Finally, virtual experiments confirm that particle dynamics are evidently susceptible to winds and initial locations. Overall, both the inclusion of physics effects (e.g., adding winds, heat fluxes, and waves) and diminishing the model uncertainties (e.g., from various wind data sources, wind drag coefficient formulations, model grids, and vertical turbulent mixing parameterizations) are important methods to improve the particle simulations. The successful application of this nearshore observation‐modeling system to Lake Michigan can be beneficial to the understanding of nearshore‐offshore transports and larval and fisheries recruitment success in similar freshwater and estuarine environments.
Plain Language Summary
We combine surface drifter observations and computer simulations to understand movements of particles in the nearshore of Lake Michigan. Analysis of drifters' moving paths, wind, and current measurements in the summers of 2014 and 2015 indicates that drifter movements are strongly related to winds and initial releasing sites. Multiple computer programs were run to simulate the designed scenarios by artificially removing one of the influencing factors at each simulation from the model. We find that particle movements are collectively affected by winds, heat flux transfer between the air and lake water, and surface gravity waves. Further investigations demonstrate that both wave‐induced sea surface roughness and Stokes drift advection are important to the simulated particle trajectories during wind events. After doing “what‐if” scenarios in the computer model, distinct particle routes driven by wind‐induced surface flows were simulated for virtual particles hypothetically released at various locations near the lake's southeastern coast. For example, nearshore group primarily shows longshore movements, while offshore one drifts into the deep basin. Overall, both the inclusion of physics effects (e.g., adding winds, heat fluxes, and waves) and diminishing the model uncertainties (e.g., from various wind data sources, wind drag coefficient formulations, model grids, and vertical turbulent mixing parameterizations) are important methods to improve the particle simulations. The outcome from this study will enhance the understanding of larval transport from nearshore to offshore areas in Lake Michigan and similar freshwater and estuarine environments.
Key Points
A wave‐current coupled particle model combined with surface drifter observations were applied to reveal nearshore particle dynamics
Winds dominate nearshore particle dynamics while effects of heat flux and waves are important as well
Including physical effects and diminishing uncertainties are important to the improved model performance</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2019JC015765</doi><tpages>26</tpages><orcidid>https://orcid.org/0000-0002-3013-4849</orcidid><orcidid>https://orcid.org/0000-0002-4295-385X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Advection Aerodynamics Brackishwater environment Computational fluid dynamics Computer programs Computer simulation Computer software Computers Current observations Data sources Drag Drag coefficient Drag coefficients Drift drifter observation Drifters Estuaries Estuarine environments Fisheries Formulations Freshwater Freshwater fish Freshwater lakes Geophysics Gravity waves Heat Heat flux Heat transfer Inland water environment Lakes Larvae Mathematical models Methods Modelling Nearshore dynamics numerical modeling Offshore Particle dynamics Particle trajectories Physics Sea surface Sea surface roughness Simulation Software Stokes drift Strong winds Surface drifters Surface gravity waves Surface roughness Trajectory control Turbulent mixing Uncertainty Wave analysis Wave effects waves Wind Wind effects Wind measurement Winds |
title | Particle Dynamics in the Nearshore of Lake Michigan Revealed by an Observation‐Modeling System |
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