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10 results for “mesoscale ocean eddies”
The Mixed Layer Depth in the Ocean Model Intercomparison Project (OMIP): Impact of Resolving Mesoscale Eddies: supporting data
<p>This file contains a jupyter notebook (python language) used to produce the figures of a manuscript submitted to the journal Geoscientific Model Development, and the data necessary to reproduce the figures.</p> <p>Abstract of the manuscript:</p> <p>The ocean mixed layer is the interface between the ocean interior and the atmosphere or sea ice, and plays a key role in climate variability. It is thus critical that numerical models used in climate studies are capable of a good representation of the mixed layer, especially its depth. Here we evaluate the mixed layer depth (MLD) in six pairs of non-eddying (1° resolution) and eddy-rich (up to 1/16°) models from the Ocean Model Intercomparison Project (OMIP), forced by a common atmospheric state. For model validation, we use an updated MLD dataset computed from observations using the OMIP protocol (a constant density threshold). In winter, low resolution models exhibit large biases in the deep water formation regions. These biases are reduced in eddy-rich models but not uniformly across models and regions. The improvement is most noticeable in the mode water formation regions of the northern hemisphere. Results in the Southern Ocean are more contrasted, with biases of either sign remaining at high resolution. In eddy-rich models, mesoscale eddies control the spatial variability of MLD in winter. Contrary to a hypothesis that the deepening of the mixed layer in anticyclones would make the MLD larger globally, eddy-rich models tend to have a shallower mixed layer at most latitudes than coarser models do. In addition, our study highlights the sensitivity of the MLD computation to the choice of a reference level and the spatio-temporal sampling, which motivates new recommendations for MLD computation in future model intercomparison projects.</p>
Dataset for "Mesoscale Eddy-Induced Sharpening of Oceanic Tracer Front"
<p>Data of the tracer experiments and the associated diagnostics in the shallow water model for the ocean front study.</p><ul><li><strong>eforc.tar.gz</strong>: diagnosed eddy forcing fields for different tracers;</li><li><strong>exps_trs.tar.gz</strong>: solutions in offline tracer experiments on the coarse grid;</li><li><strong>forc_uvh.tar.gz</strong>: mass fluxes and layer thicknesses used to advect tracers;</li><li><strong>params.tar.gz</strong>: parameters used for tracer experiments</li></ul><p>Please contact Yueyang Lu via <strong>yueyang.lu@miami.edu</strong> if there are any questions.</p>
The Heat and Carbon Characteristics of Modelled Mesoscale Eddies in the South Atlantic Ocean
<p>Datasets in this repository are generated from BIOPERIANT12-CNCLNG01 model and are part of the manuscript entitled: "The Heat and Carbon Characteristics of Modelled Mesoscale Eddies in the South Atlantic Ocean". </p>
Data for "Properties of the lateral mesoscale eddy-induced transport in a high-resolution ocean model: Beyond the flux-gradient relation" (Lu et al. JPO)
<p>Preprocessed data to reproduce results and figures in "Properties of the lateral mesoscale eddy-induced transport in a high-resolution ocean model: Beyond the flux-gradient relation" (Lu et al., In Review of <em>Journal of Physical Oceanography</em>). </p> <p>Feel free to contact Yueyang Lu via <strong>yxl1496@miami.edu</strong> if you have any questions.</p>
Script and data of "Role of Frictional Processes in Mesoscale Eddy Available Potential Energy Budget in the Global Ocean"
<p>% File description:</p> <p>1. Cal_conversions.m: a set of functions calculating the EAPE-EKE and EAPE-EKE conversion terms with CESM output data in B-grid</p> <p>2. smooth2a.m: function of boxcar filtering</p> <p>3. CONV_u100_2d.mat: data of the global distribution of upper 100 m averaged conversion terms used in Figure 2 of the manuscript<br> % Variables inside the file:<br> CONVa_H_u100: MAPE-EAPE conversion driven by frictional process<br> CONVo_H_u100: MAPE-EAPE conversion driven by non-frictional process<br> CONVa_V_u100: EAPE-EKE conversion driven by frictional process<br> CONVo_V_u100: EAPE-EKE conversion driven by non-frictional process</p> <p>4. CONV_profile.mat: data of the vertical profiles of global and regional averaged EAPE-EKE conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_profile: driven by frictional process<br> CONVo_V_GLO_profile: driven by non-frictional process<br> CONVttw_V_GLO_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_profile: driven by frictional process<br> CONVo_V_WBCE_profile: driven by non-frictional process<br> CONVttw_V_WBCE_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_profile: driven by frictional process<br> CONVo_V_STG_profile: driven by non-frictional process<br> CONVttw_V_STG_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_profile: driven by frictional process<br> CONVo_V_SPG_profile: driven by non-frictional process<br> CONVttw_V_SPG_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_profile: driven by frictional process<br> CONVo_V_SO_profile: driven by non-frictional process<br> CONVttw_V_SO_profile: reproduced by TTW balance </p> <p>5. CONV_SeasDiff.mat: data of the seasonal difference (winter minus summer) of global and regional averaged conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of the seasonal difference of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_SeasDiff: driven by frictional process<br> CONVo_V_GLO_SeasDiff: driven by non-frictional process<br> CONVttw_V_GLO_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_SeasDiff: driven by frictional process<br> CONVo_V_WBCE_SeasDiff: driven by non-frictional process<br> CONVttw_V_WBCE_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_SeasDiff: driven by frictional process<br> CONVo_V_STG_SeasDiff: driven by non-frictional process<br> CONVttw_V_STG_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_SeasDiff: driven by frictional process<br> CONVo_V_SPG_SeasDiff: driven by non-frictional process<br> CONVttw_V_SPG_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_SeasDiff: driven by frictional process<br> CONVo_V_SO_SeasDiff: driven by non-frictional process<br> CONVttw_V_SO_SeasDiff: reproduced by TTW balance </p> <p>6. Coord_lon_lat_zw.mat: coordinate information for the variables in "CONV_u100_2d.mat", "CONV_profile.mat"and "CONV_SeasDiff.mat"<br> % Variables inside the file:<br> lon: longitude for the global distributions of the conversion terms<br> lat: latitude for the global distributions of the conversion terms<br> z_w: depth of each vertical level for vertical profiles of conversion terms</p>
Additional Supporting Information to 'Quantifying the Contribution of Ocean Mesoscale Eddies to Low Oxygen Extreme Events.'
<p>Additional Supporting Information to 'Quantifying the Contribution of Ocean Mesoscale Eddies to Low Oxygen Extreme Events.'. Submitted to Geophysical Research Letters for publication. 2022.</p> <p>NetCDF files and Python NumPy arrays of data used to create all figures in the manuscript main text and supporting information.</p>
Data from: A daily global mesoscale ocean eddy dataset from satellite altimetry
Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993–2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System.
Dataset for paper Pavel Perezhogin, Andrey Glazunov "Subgrid parameterizations of ocean mesoscale eddies based on Germano decomposition" submitted to JAMES.
<p>The data is updated due to the response to review in the third round.</p> <p>The directory structure is :</p> <pre><code>├── barotropic │ ├── data │ ├── figures │ └── code ├── pyqg │ ├── data │ ├── figures │ └── code ├── NEMO │ ├── data │ ├── figures │ └── code </code></pre> <p>Where <strong>barotropic</strong> - experiments in barotropic fluid, <strong>pyqg</strong> - experiments in QG model, <strong>NEMO</strong> - experiments in primitive equation model.</p> <p>Subfolder <strong>data</strong> contains numerical simulations, <strong>figures</strong> contains plotters of Figures for Publication, and <strong>code</strong> is source code with parameterized barotropic/QG/primitive equation ocean models. </p>
Data from: A daily global mesoscale ocean eddy dataset from satellite altimetry
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Dataset for "Mesoscale Eddy-Induced Sharpening of Oceanic Tracer Front and its Parameterization"
<p>Data of the tracer experiments and the associated diagnostics in the shallow water model for the ocean front study.</p><ul><li><strong>eforc.tar.gz</strong>: diagnosed eddy forcing fields for different tracers;</li><li><strong>exps_trs.tar.gz</strong>: solutions in offline tracer experiments on the coarse grid;</li><li><strong>forc_uvh.tar.gz</strong>: mass fluxes and layer thicknesses used to advect tracers;</li><li><strong>params.tar.gz</strong>: parameters (eddy-induced velocities, diffusivity) used for tracer experiments</li></ul><p>Please contact Yueyang Lu via <strong>yueyang.lu@miami.edu</strong> if there are any questions.</p>
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International Brain Laboratory public data
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OpenNeuro
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