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Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data
<p><strong>Abstract:</strong></p> <p>This data set contains model output data from the regional ocean modelling system (ROMS) that was set up for the Southern Ocean (south of 24 °S; Haumann, 2016) to analyze the effects of changing surface freshwater and momentum fluxes on Southern Ocean water-mass changes over the period 1980 to 2011. All data is provided in NetCDF format. The data set contains 3 sets 40-year long model spin-up, control, and 3 perturbation simulations each. The sets differ by their model mean state to assess the effect of surface salinity biases on the results. One set of simulations are reference simulations where salinity is restored to the observed surface salinity during model spin-up, and the other two sets are simulations in which the restoring surface salinity has been altered by plus and minus 0.1 PSU, respectively. The control and perturbation simulations are 40-year extensions of the respective model spin-up simulations. In the perturbation simulations either the surface freshwater fluxes or momentum fluxes are instantaneously perturbed after the spin-up simulation to reflect the observation-derived changes in these surface fluxes and then held constant (at the perturbed level) for 40 years. They consist of sea-ice freshwater flux, glacial meltwater flux, and atmosphere-ocean momentum flux changes. Details on the model setup, forcing, and simulations can be obtained from the related research article by Haumann et al. (2020; https://doi.org/10.1029/2019AV000132).</p> <p><br> <strong>Contacts:</strong></p> <p>F. Alexander Haumann, ORCID: 0000-0002-8218-977X. Email: alexander.haumann@gmail.com</p> <p><br> <strong>License:</strong></p> <p>When using this data users must cite the original research article by Haumann et al. (2020) published in AGU Advances (https://doi.org/10.1029/2019AV000132). This model output data set is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p><br> <strong>Citations:</strong></p> <p>Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice Induced Southern Ocean Subsurface Warming and Surface Cooling in a Warming Climate. AGU Advances, 1, e2019AV000132. https://doi.org/10.1029/2019AV000132</p> <p>Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3709154</p> <p><br> <strong>Data processing:</strong></p> <p>The model data provided here has been processed for analysis and, due to storage constraints, only variables and averages used in the related research article (Haumann et al., 2020) are published here. Years refer to model years since initiation, i.e. the start of the spin-up simulation, and reach a maximum of 80 at the end of the control and perturbation experiments. Averages are either annual means over an indicated period ('yearly'), overall means over an indicated period ('mean'), or monthly climatologies over an indicated period ('clim'). 'Yearly' and 'Clim' values are provided either for surface fields or vertically integrated fields ('surf'). 'Mean' values are provided on the native vertical model grid as 3D fields. 'Yearly' zonal mean ('zonalmean') values have been first interpolated to from the native vertical model grid to a regular vertical z-level grid and then zonally averaged. All data has been stored in NetCDF format and compressed using level 1 deflation (https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression). Full monthly mean model output from these simulations and the forcing fields can be obtained from the corresponding author upon request (alexander.haumann@gmail.com).</p> <p><br> <strong>Standards:</strong></p> <p>Data files have been formatted as Network Common Data Form (NetCDF; https://www.unidata.ucar.edu/software/netcdf/) in Hierarchical Data Format, version 5 (https://portal.hdfgroup.org/display/knowledge/HDF5+Documentation)</p> <p><br> <strong>Contents:</strong></p> <p><em>Data</em><br> - ROMS_SO_d025_grd.nc, ROMS grid file (Note: one row of ghost points on either side of the grid), compressed netcdf-file<br> - ROMS_SO_d025_ref_spinup, ROMS output from reference spin-up simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_ref_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_ref_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_ref_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file<br> - ROMS_SO_d025_minus01_spinup, ROMS output from minus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_minus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_minus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_minus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file<br> - ROMS_SO_d025_plus01_spinup, ROMS output from plus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_plus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_plus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_plus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file<br> - ROMS_SO_d025_ref_ctrl, ROMS output from reference control simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_ctrl, ROMS output from minus 0.1 PSU sensitivity control simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_ctrl, ROMS output from plus 0.1 PSU sensitivity control simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_ref_seaice, ROMS output from reference sea-ice freshwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_seaice, ROMS output from minus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_seaice, ROMS output from plus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_ref_glacial, ROMS output from reference glacial meltwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_glacial, ROMS output from minus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_glacial, ROMS output from plus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_ref_momentum, ROMS output from reference atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_momentum, ROMS output from minus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_momentum, ROMS output from plus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> <br> <em>File headers</em><br> - ROMS_SO_d025_grd:<br> angle [radians]: angle between xi axis and east<br> f [second-1]: Coriolis parameter at RHO-points<br> h [meter]: Final bathymetry at RHO-points<br> hraw [meter]: Working bathymetry at RHO-points<br> lat_rho [degree_north]: latitude of RHO-points<br> lat_psi [degree_north]: latitude of PSI-points<br> lat_u [degree_north]: latitude of U-points<br> lat_v [degree_north]: latitude of V-points<br> lon_rho [degree_east]: longitude of RHO-points<br> lon_psi [degree_east]: longitude of PSI-points<br> lon_u [degree_east]: longitude of U-points<br> lon_v [degree_east]: longitude of V-points<br> mask_rho [-]: mask on RHO-points<br> mask_u [-]: mask on U-points<br> mask_v [-]: mask on V-points<br> pm [meter-1]: curvilinear coordinate metric in XI<br> pn [meter-1]: curvilinear coordinate metric in ETA<br> spherical [char]: Grid type logical switch<br> - ROMS_SO_d025_*_clim.*.surf.nc, ROMS_SO_d025_*_yearly.*.surf.nc:<br> time [days since 0000-01-01 00:00:00]: time<br> lon [degree_east]: longitude<br> lat [degree_north]: latitude<br> zeta [meter]: averaged free-surface elevation<br> temp [degree_celsius]: averaged potential temperature<br> salt [psu]: averaged salinity<br> rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3<br> mld [meter]: averaged mixed layer depth<br> n2 [seconds-2]: averaged buoyancy frequency (upper 100 m)<br> n2temp [seconds-2]: averaged buoyancy frequency due to temperature (upper 100 m)<br> n2salt [seconds-2]: averaged buoyancy frequency due to salinity (upper 100 m)<br> heat_100 [10^18 J]: averaged ocean heat content (upper 100 m)<br> heat_100_2000 [10^18 J]: averaged ocean heat content (between 100 m and 2000 m)" ;<br> - ROMS_SO_d025_*_mean.*.nc:<br> time [days since 0000-01-01 00:00:00]: time<br> lon [degree_east]: longitude<br> lat [degree_north]: latitude<br> zeta [meter]: averaged free-surface elevation<br> temp [degree_celsius]: averaged potential temperature<br> salt [psu]: averaged salinity<br> rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3<br> - ROMS_SO_d025_*_yearly.zlev.zonalmean.*.nc:<br> time [days since 0000-01-01 00:00:00]: time<br> lat [degree_north]: latitude of RHO-points<br> lat_v [degree_north]: latitude of V-points<br> depth [meter]: depth of levels<br> temp [degree_celsius]: averaged potential temperature<br> salt [psu]: averaged salinity<br> rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3<br> moc [10^6 meter^3 second^-1]: averaged meridional overturning circulation on V-points<br> Missing values: missing values are filled as '-9.99e+20'.</p> <p><em>Metadata</em><br> - README.txt, metadata, text format</p> <p><br> <strong>References:</strong></p> <p>Haumann, F. A. (2016): Southern Ocean response to recent changes in surface freshwater fluxes. Doctoral Thesis. ETH Zurich. doi:10.3929/ethz-b-000166276.</p> <p><br> <strong>Acknowledgments:</strong></p> <p>This work was supported by ETH Research Grant CH2-01 11-1 and by the SNSF grant numbers P2EZP2_175162 and P400P2_186681, as well as NSF’s SOCCOM Project under NSF Award No. PLR-1425989.</p>
Future Ocean Warming May Threaten Key Photosynthetic Microbes
<h2>Description</h2> <p>The datasets supporting the conclusions of this article, including field measurements of <em>Prochlorococcus</em> division rates, are available in this repository. </p> <p>The R code performs the following tasks:</p> <ul> <li>Loads data from various sources, including lab experiments, dilution experiments, and in-situ measurements.</li> <li>Calculates thermal norm predictions using different models (Eppley, Hinshelwood, Eppley-Norberg) to predict division rates based on temperature.</li> <li>Generates figures to visualize the results, including latitudinal and temperature effects on division rates, model predictions compared with observed data, and changes in primary production under different emission scenarios.</li> <li>Fits the Hinshelwood model to culture data and extracts best-fit parameters.</li> <li>Performs bootstrapping to estimate uncertainty in the Hinshelwood model parameters.</li> <li>Calculates confidence intervals for the bootstrapped parameters.</li> </ul> <h2>R Scripts</h2> <ul> <li><strong>Ribalet_main.R:</strong> This script contains the main analysis code, including data loading, model fitting, figure generation, and bootstrapping.</li> <li><strong>Ribalet_fitting.R:</strong> This script defines functions for fitting different growth models to the data and estimating model parameters.</li> </ul> <h2>Requirements</h2> <ul> <li>R version 4.4.2 (2024-10-31)<br>Platform: aarch64-apple-darwin20<br>Running under: macOS Sequoia 15.1.1</li> <li>Matrix products: default<br>BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib <br>LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0</li> <li>attached base packages:<br>[1] parallel stats graphics grDevices utils datasets <br>[7] methods base </li> <li>other attached packages:<br> [1] DEoptim_2.2-8 arrow_15.0.1 ggpubr_0.6.0 lubridate_1.9.3<br> [5] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 <br> [9] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 <br>[13] tidyverse_2.0.0</li> </ul> <h2>Installation</h2> <p>Install the required R packages:</p> <div> <div>Code snippet</div> <div> <div> <pre><code>install.packages(c("tidyverse", "ggpubr", "arrow", "DEoptim")) </code></pre> </div> </div> </div> <h2>Usage</h2> <p>The scripts will generate figures and output files in the same directory.</p> <h2>Input Data</h2> <p>The code requires the following input data files:</p> <ul> <li>culture.csv</li> <li>dilution.csv</li> <li>abundance.csv</li> <li>mpm.csv</li> <li>model_results.parquet</li> <li>bootstrap_projections.csv</li> <li>modeled-thermal-traits.tsv</li> <li>sst.parquet</li> <li>culture_syn.csv</li> </ul> <p>Please ensure that these files are present in the same directory as the R script files.</p> <h2>Output Data</h2> <p>The code generates the following output files:</p> <ul> <li>Figures: Figure1.png, Figure2.png, Figure3.png, FigureS1.png, FigureS2.png, FigureS3.png, FigureS4.png, FigureS5.png, FigureS6.png, FigureS9.png, FigureS11.png, FigureS12.png, FigureS13.png, FigureS14.png, FigureS15.png</li> <li>CSV files: bootstrap_parameters.csv, cultures_thermal_reactions.csv</li> </ul> <h2>License</h2> <p>This code is licensed under the MIT License.</p>
Coral calcification mechanisms in a warming ocean and the interactive effects of temperature and light
<p>Ross et al 2022 Supplementary data for coral (<em>Acropora nasuta</em>) temperature and light experiments. </p>
Code and data for "Contrasting upper and deep ocean oxygen response to protracted global warming," by Frölicher et al., Global Biogeochemical Cycles, 34, e2020GB006601: https://doi.org/10.1029/2020GB006601
<p>This file contains the data and python/NCL scripts that have been used for the analysis in this paper. </p>
Model Data for "Increased Ocean Heat Convergence into the High Latitudes with CO2-Doubling Enhances Polar-Amplified Warming."
<p>This is the data repository for the following published study:</p> <p>Singh HA, Rasch PJ, and Rose BEJ. "Increased Ocean Heat Convergence into the High Latitudes with CO<sub>2</sub>-Doubling Enhances Polar-Amplified Warming", Geophysical Research Letters, Oct 2017, doi: 10.1002/2017GL074561.</p> <p>Please see 'README.txt' for further details on the data files included.</p>
Time-varying global energy budget since 1880 from a reconstruction of ocean warming
<p>This dataset was produced as part of the research presented in the paper "Time-varying global energy budget since 1880 from a reconstruction of ocean warming" published in PNAS. For detailed methodology, analysis, and interpretation of the data, please refer to the original publication: 10.1073/pnas.2408839122.</p>
Storyline Simulations Data for the paper Athanase et al.: Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean
<p>Data used for producing the Figures in the paper entitled "Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean", Athanase et al. (Communications Earth & Environment).</p> <p>The AWI-CM-1-1-MR free runs are available in the Earth System Grid Federation (ESGF) data nodes (https://esgf-data.dkrz.de/search/cmip6-dkrz/). The ERA5 reanalysis data used in the paper can be accessed from the European Centre for Medium-Range Weather Forecasts (ECMWF; https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Here, we provide data from the nudged storyline simulations carried out with the AWI-CM-1-1-MR coupled climate model.</p> <p>Parameters naming convention:</p> <p>- Sea Surface Temperature ("tos").</p> <p>- Radiative Fluxes ("radiations"), including net surface heat flux ("qnet"), net surface thermal radiation ("trads"), net surface solar radiation ("srads"), latent heat flux ("ahfl"), sensible heat flux ("ahfs").</p> <p>- Low Clouds Cover ("lcc").</p> <p>- Mixed Layer Depth ("mlotst").</p> <p>- Surface Air Temperature ("tas").</p> <p>- 10 m winds ("u10","v10").</p> <p>All data is provided as the 5-member ensemble mean from the nudged storyline simulations. Data is provided for the storyline simulations of the summer 2019 Northeast Pacific marine heatwave, in different background climate conditions: preindustrial ("PI"), present-day ("PD"), and +4°C warmer world ("4K"). </p> <p> </p>
F I G U R E 6 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 6 Back-calculated mean body length (±95% confidence interval) of Salmo salar at the midpoint of the winter annulus, following the conclusion of the post-smolt growth period
F I G U R E 1 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 1 Hierarchical cluster analysis of intercirculus spacing for scales of Salmo salar. (a) The dendrogram for k = 20 using Euclidean distance and Ward linkage for z-scored and interpolated data. The five major sub-branches (A–E) and the 20 clusters are ordered sequentially from the left. (b) The standardized intercirculus spacing plots for the 20 clusters. Clusters are colour-coded and ordered as in (a). The LOESS fits for each cluster are shown as a black line and the number of fish per cluster (n) is also shown
F I G U R E 2 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 2 Tabulation of significant under- and over-representation of the 10 most frequent growth pattern categories (and "Others") for Salmo salar scales amongst the 20 dendrogram clusters. Proportions of growth pattern frequency were compared to the overall population proportion of scales for k = 20 with Ward linkage, and clustering of the z-scored and interpolated data. Light shading (−) indicates significant under-representation and dark shading (+) indicates over-representation. Sample sizes (n) for each growth pattern across the time-series are shown
F I G U R E 4 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 4 Time-series changes in ocean surface temperature and Salmo salar scale growth pattern. (a) Changes in monthly SST anomaly for the 250 and 500 km standard deviation spatially weighted kernels in the Norwegian Sea (April 1992 – March 2011). (b) Changes in frequency (proportion within years) of selected growth patterns. The three selected pattern groupings illustrate fish showing persistent Fast growth (F) throughout the post-smolt growth season, Slow growth followed by Fast growth (SF), and all patterns pooled that displayed one or more growth Checks. The growth pattern data for each capture year (b) are aligned with the SST anomaly in April of the previous year (a), coinciding with the commencement of annual smolt emigration
F I G U R E 5 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 5 Monthly correlations between the SST anomalies throughout the post-smolt Salmo salar growth period and annual frequency of the Fast (F) and All Check growth patterns. The salmon data were lagged by -1 year to match the annual post-smolt growth seasons to the SST anomalies. Significant correlations (P <0.05; following adjustment of d.f. to allow for autocorrelation) are shown by the filled circles
Figures 2 and 3 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
Figures 2 and 3 summarize the under/over-representation of the more common growth patterns and years, respectively, amongst the k = 20 clusters. The SF growth pattern was over-represented for four of the five clusters of sub-branch A, in association with general underrepresentation of patterns showing an initial Fast (F) sequence (Figure 2). Sub-branch B revealed an essentially inverse structure to sub-branch A, with sporadic over-representation of patterns commencing with a Fast sequence and under-representation of those with an initial Slow sequence. Sub-branches C and D were heterogeneous
F I G U R E 3 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 3 Tabulation of significant under- and overrepresentation of the 20 dendrogram clusters amongst years of capture of return adult Salmo salar. Details as for Figure 2
Figure 4b in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
Figure 4b shows that there was (a) a decrease in the frequency of fish showing consistently Fast growth throughout the post-smolt period, (b) an increase in the frequency of the SF growth pattern and (c) an increase in the frequency of growth patterns including one or more Check sequences. Furthermore, these time-series changes in circulus pattern were linked significantly to contemporaneous and anomalous warming of the Norwegian Sea (Figure 5). As shown in Figure 6, one proximate consequence of these changes is manifest in the backcalculated mean length of fish at the midpoint of the winter annulus, following the completion of the post-smolt growth season. This showed a marked and significant decrease across the final six capture years of the time series.
Future seasonal changes in habitat for Arctic whales during predicted ocean warming
<p><span>Ocean warming is causing shifts in the distributions of marine species, but the location of suitable habitats in the future is unknown, especially in remote regions such as the Arctic. Using satellite tracking data from a 28-year long period, covering all three endemic Arctic cetaceans (227 individuals) in the Atlantic sector of the Arctic, together with climate models under two emission scenarios, species distributions were projected to assess responses of these whales to climate change by the end of the century. While contrasting responses were observed across species and seasons, long-term predictions suggest northward shifts (243 km in summer vs. 121 km in winter) in distribution to cope with climate change. Current summer habitats will decline (mean loss: </span><span>-</span><span>25%), while some expansion into new winter areas (mean gain: +3%) is likely. However, comparing gains vs. losses raises serious concerns about the ability of these polar species to deal with the disappearance of traditional colder habitats.</span></p>
Observed Indian Ocean Warming Trend Applied to the IPSL-CM6A-LR model
<p>This experiment applies the observed warming trend of the tropical Indian Ocean (0.15 deg C per decade; Hu and Fedorov, 2019) to the tropical Indian Ocean region in the IPSL-CM6A-LR pre-industrial control run. The methods of nudging are defined in Ferster et al. (2021), where here we apply an observed warming rate rather than a constant (as in Ferster et al., 2021).</p> <p>This experiment relates to the submitted article entitled <em>Variations in tropical Indian Ocean SST drive multi-decadal AMOC variability in models and observations</em>.</p> <p>*The datasets are created from the IPSL-CM6A-LR output files</p> <p>**This version serves as a preliminary version of the dataset for publication purposes, additional output is available.</p>
CESM2 MDM data for "Historical changes in wind driven ocean circulation can accelerate global warming" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MDM in paper), CESM2</li> <li>FC = fully coupled model (referred to as FCM in paper), CESM2</li> </ul> <p>Decoding file names:</p> <p>Variables that are a single value per time step (e.g. global means and globally integrated values) are given in dimensions of time by ensemble member. Variables that include values at every grid point at each point in time are provided with an ensemble mean trend and an ensemble standard deviation of the trend. </p> <ul> <li>ensmean refers to ensemble mean</li> <li>ensstd refers to ensemble standard deviation</li> <li>trend refers to linear trend over 1979-2014</li> <li>annual refers to annual mean anomalies, relative to reference period of 1941-1970</li> </ul> <p>Variables:</p> <ul> <li>aice = ice area</li> <li>AMOC = Atlantic meridional overturning circulation</li> <li>N_HEAT = northward heat transport </li> <li>BSF = barotropic streamfunction </li> <li>TREFHT = reference level air temperature </li> <li>Qnet = net surface heat flux (defined as FSNS - FLNS - LHFLX - SHFLX)</li> <li>TOA = top of atmosphere radiation </li> <li>TOAC = top of atmosphere radiation, clearsky </li> <li>FLNT = net longwave flux at top of model</li> <li>FLNTC = net longwave flux at top of model, clearsky</li> <li>FSUTOA = upwelling solar flux at top of atmosphere</li> <li>FSNTOA = net solar flux at top of atmosphere</li> <li>FSNTOAC = net solar flux at top of atmosphere, clearsky</li> </ul> <p> </p> <p> </p> <p> </p>
High-end projections of Southern Ocean warming and Antarctic ice shelf melting in conditions typical of the end of the 23rd century
<h2><strong>High-end projections of Southern Ocean warming and Antarctic ice shelf melting in conditions typical of the end of the 23rd century</strong></h2> <p>To evaluate the response of the Southern Ocean and Antarctic ice shelf cavities to an abrupt change to high-end atmospheric<br>conditions typical of the late 23rd century under the SSP5-8.5 scenario, in Mathiot and Jourdain (2023, submitted soon), we conducted 2 experiments. Our reference experiment (called REF) is driven by present day atmospheric condition. In the 23rd century simulation (called PERT), the present day atmospheric forcing is perturbed by the anomaly (2260-2299 minus 1975-2014) extracted from monthly outputs of the IPSL-CM6A-LR projections under the SSP5-8.5 emission scenario. REF is run over the latest 40 years and PERT is run for 100y starting from PERT at year 1999.</p> <p><strong>This data set contains:</strong></p> <ul> <li>The atmospheric forcing anomalies used to perturbed our reference atmospheric forcing in the PERT simulation;</li> <li>30y monthly climatologies of multiple variables (ocean temperature, salinity, ssh, velocities, barotropic stream function, iceberg melt, ice shelf melt, sea ice concentration, thickness, velocities and snow thickness) for PERT and REF.</li> </ul> <p>All the details on each dataset have been added in separated README in ATMO_ANOMALIES and OCEAN_CLIMATOLOGIES directory.</p> <p>As stated in each README, all the detailed on the simulations and atmospheric perturbation are available in Mathiot and Jourdain (2023).</p> <p><strong>Changes with respect to v1.0.0:</strong> iceberg melt and ice shelf melt climatologies for REF and PERT has been added to the dataset (*flxT.nc files).</p> <p><strong>Reference paper:</strong> Mathiot, P. and Jourdain, N. C.: Southern Ocean warming and Antarctic ice shelf melting in conditions plausible by late 23rd century in a high-end scenario, Ocean Sci., 19, 1595–1615, https://doi.org/10.5194/os-19-1595-2023, 2023.</p> <div> <div><strong>Acknowledgements:</strong> This study was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 820575 (TiPACCs) and by the French National Research Agency under grant no. ANR-19-CE01-0015 (EIS). N. Jourdain was also supported by EU-H2020 grant nos. 101003536 (ESM2025) and 869304 (PROTECT). This work was granted access to the high-performance computing (HPC) resources of CINES and TGCC under allocations A0100106035 and A0120106035 attributed by GENCI.</div> </div>
Data used in "Storms regulate Southern Ocean summer warming"
<p>The data included in this repository was used to generate the figures in the submitted manuscript "Storms regulate Southern Ocean summer warming" by du Plessis and co-authors.</p> <p><strong>Abstract: </strong>"Sea surface temperature (SST) in the Southern Ocean (SO) is the fingerprint of ocean heat uptake and critical for air-sea interactions. However, SO SST is biased warm in climate models, reflecting our limited understanding of the mechanisms that set its magnitude and variability. An important factor driving SST variability is synoptic-scale weather systems, such as storms, yet their impacts are difficult to directly observe. Using in-situ observations from underwater and surface robotic vehicles in the subpolar SO, we show evidence that storms regulate the summer evolution of SST through altering the mixed layer effective heat capacity and entraining colder water from below. Through these mechanisms, we determine that interannual variations in SO SST reflect changes in storm intensity and prevalence, which, in turn, are driven by the Southern Annular Mode. Our results demonstrate a causal link between storm forcing and lower frequency SST variability, which has implications for addressing SST biases in climate models."</p> <h3><strong>Datasets</strong></h3> <p>The observations in this study were made as a part of the SOSCEx-STORM experiment, which fits into the larger observational programme the Southern Ocean Seasonal Cycle Experiment (Swart et al. 2012). SOSCEx-STORM undertook a twinned deployment of a Wave Glider and a profiling Slocum glider which were piloted in conjunction with each other. The platforms were deployed and retrieved from the R/V Agulhas II at 54°S, 0°E, south of the Polar Front, and sampled together between 20 December 2018 and 8 March 2019. </p> <p><strong>Slocum glider data<br></strong>The glider was equipped with a continuously pumped Seabird Slocum Glider CTD, which was processed with the GEOMAR MATLAB toolbox and vertically gridded to 1 m depth intervals. </p> <p>Relevant data name: <code>slocum_grid_processed.nc</code></p> <p><em>Slocum glider Microstructure data:</em><br>The Webb Teledyne G2 Slocum glider was equipped with a Rockland Scientific Microstructure Profiler (MicroRider). The MicroRider was equipped with two piezo-electric accelerometers and two air-foil shear probes oriented orthogonally. Microstructure data was only collected during the glider climbs to prolong battery life and obtain dissipation estimates as close to the surface as possible. See Nicholson et al. (2022) for details of the MicroRider processing. The mixing layer depth (XLD) was estimated as in Brainnerd and Gregg et al. (1995).</p> <p>Disspitation data name: <code>slocum_eps.nc</code><br>Mixing layer depth data name:<em> </em><code>slocum_xld.nc</code></p> <p><em>Slocum glider SST data:</em> Initial data processing removed temperature data from the upper 2 m during the glider climb phase, and so to obtain an SST value from the Slocum glider temperature profiles, we calculated the median value between 0.5 m and 10 m depth for each dive. </p> <p>Slocum SST data name: <code>slocum_sst_median_10m.nc</code></p> <p><strong>Wave Glider data<br></strong>The Liquid Robotics SV3 Wave Glider was fitted with an Airmar WX-200 Ultrasonic Weather Station mounted on a mast at 0.7 m above sea level, providing wind speed measurements at a rate of 1 Hz, averaged into 1-hour bins. The wind measurements were corrected to a height of 10 m above sea level. Note that the Airmar WX-200 weather station of the Wave Glider was faulty and the wind speed, wind direction and wind stress data was replaced by hourly ERA5 data. </p> <p>Wave Glider data name: <code>WG_era5_1h_processed_28Aug2022.nc</code></p> <p><strong>NOAA OI SST and sea ice<br></strong>Monthly SST data was obtained from the NOAA optimum interpolation (OI) SST V2 product, which uses both in-situ and satellite data from November 1981 to January 202329. Data is provided by the National Centers for Environmental Prediction and made available on a 1◦ grid. All SST data where co-located sea ice concentration was above 0 has been removed from this analysis. NOAA OI SST and sea ice were obtained from<a href="https://psl.noaa.gov/data/gridded/data.noaa"> https://psl.noaa.gov/data/gridded/data.noaa</a><strong>.<br></strong></p> <p>Datasets: <code>sst.mnmean.nc</code>, <code>icec.mnmean.nc</code>, <code>lsmask.nc</code></p> <p><strong>Storm tracking dataset</strong><br>To track storm trajectories, we used storm tracks contained in monthly files for the Southern Ocean identified and used in the JGR-Oceans publication:</p> <p>Lodise, J., Merrifield, S. T., Collins, C., Rogowski, P., Behrens, & J., Terrill,E, (In Review). Global Climatology of Extratropical Cyclones From a New Tracking Approach and Associated Wave Heights from Satellite Radar Altimeter. Journal of Geophysical Research: Oceans. <a href="https://doi.org/10.1029/2022JC018925" rel="nofollow">https://doi.org/10.1029/2022JC018925</a></p> <p>Data can be accessed at <a href="https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker">https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker</a> </p> <p>All Southern Ocean storm locations can be found at: <code>ec_centers_1981_2020.nc</code></p> <p><strong>Storm radius datasets</strong></p> <p>ERA5 data of air-sea heat flux, 10 m wind speed for all hourly instances where a storm center was within 1000 km of the gliders (Figs. 2 and 3, Extended Data Figs. 2 and 4) </p> <p>Datasets of <code>combined_storms_{variable}_no_ice.nc</code> are data of air-sea heat flux and 10 m wind speed for all instances for 1000 km x 1000 km box around each storm center during summer months from 1981-2020 (> 570,000) used in Figs. 4 and 5. These data are considerably large (combined total >40 GB). Please contact me at marcel.du.plessis@gu.se to find a suitable way to share the data.</p> <p>The data was processed as follows:</p> <p>1. Download storm centers from <a href="https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker/tree/main">https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker/tree/main</a></p> <p>2. Run process-lodise-storm-centers.ipynb to save all the cyclone center data as '<code>ec_centers_1981_2020.nc</code>'</p> <p>3. <em>Run filter-cyclone-centers.ipynb</em>:<br> - cut all cyclone centers south of 40S<br> - only choose cyclone centers in DJF<br> - calculates minimum distance to land for each storm<br> - saves a dataset called '<code>ec_centers_1981_2020_with_min_dist_to_land.nc</code>' <br> - contains the variable distance to land for all filtered cyclones<br> - we do this step because it takes about an hour to run<br> - remove cyclones within 500 km from land<br> - remove cyclones less than 24 hours<br> - we are left with 11005 storms<br> - data saved as 'ec_centers_1981_2020_500km_from_land_filtered_24hours'</p> <p>4. Run storm_processing_cutouts.ipynb (this took several days)<br> - loads <code>ec_centers_1981_2020_500km_from_land_filtered_24hours.nc</code><br> - runs through each summer, <em>processes storm_localization.py </em><br> - saves data as <code>storms_{variable}_{year}.nc</code><br> - e.g. <code>storms_winds_1981.nc</code> - winds for all 1000 km radius cyclones in DJF 1981/82</p> <p>5. <em>combine_storm_years.ipyn</em>b <br> - creates datasets for each variable that has storms for all year called <code>combined_storms_{variable}.nc</code></p> <p>6. <em>remove_sea_ice_from_storms.ipnyb </em><br> - makes data nan where sea ice is present in each cyclone<br> - creates datasets called <code>combined_storms_{variable}_no_ice.nc</code></p> <p>7. <em>seasonal-means-storms.ipynb</em><br> - calculates the mean for all storms for each year <br> - saves them one dataset: <code>combined_storms_{variable}_seasonal_means.nc'</code></p> <p><strong>EN4 mixed layer depths</strong><br>We use the EN4 database of quality controlled temperature and salinity profiles from 2004 to 2022 to produce our MLD for the interannual analysis (Good et al. 2013). We use the profiles that contain the Cheng et al. (2014) XBT corrections and Gouretski and Cheng (2020) MBT corrections. We limit the data intake to 2004 as this marks the beginning of the Argo period. All under-ice profiles are removed. We calculate the MLD for each individual profile using the density threshold of de Boyer Montegut et al. (2004) where the density value first exceeds the 10 m reference value by 0.03 kg m-3. We then determine the median MLD value for each month within 3 x 3 degree grid cells, then obtain a mean value for each DJF season per 3 x 3 degree grid cell. </p> <p>Relevant data name: <code>en4_monthly_mixed_layer_depth_median.nc</code></p> <p><strong>Southern Ocean Fronts<br></strong>Position of the Subantarctic Front and Polar Front are from: </p> <div>Sokolov, S. and Rintoul, S.R., 2009. Circumpolar structure and distribution of the Antarctic Circumpolar Current fronts: 1. Mean circumpolar paths. <em>Journal of Geophysical Research: Oceans</em>, <em>114</em>(C11).</div> <div> </div> <div>Relevant data name:<em> </em><code>ACCfronts.csv</code><strong> </strong></div> <div> </div> <div><strong>ERA5<br></strong>The ERA5 data provided was by ECMWF available at <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>.</div> <div> </div> <div>The various datasets used in this study are described below:<strong><br></strong><br>Wind speed, air temperture, dew point temperature for the observational period: <code>ds_era5_vars.nc</code><br>Fluxes for the observational period: <code>ds_era5_flux.nc</code><br>Wind speed, air temperture, dew point temperature, fluxes for the case study day in Figure 3: <code>era5_case_study.nc</code><br>Mean winds and fluxes for each DJF period between 1981 and 2022.: <code>mean_summer_winds_fluxes_1981_2023.nc</code></div> <div>Monthly-mean 10 m wind speed and mean sea level pressure during SOSCEx-Storm: <code>201812_month_avg_wind_mslp.nc</code> and <code>20190102_month_avg_wind_mslp.nc</code></div> <div> </div> <div><strong>Cloud Top Pressure<br></strong>The MODIS Level-2 Cloud product was obtained from <a href="http://dx.doi.org/10.5067/MODIS/MYD06_L2.061">http://dx.doi.org/10.5067/MODIS/MYD06_L2.061</a> (Fig. 3).<strong><br></strong></div> <div> <p>Processed dataset: <code>modis_ctt_ctp.nc</code></p> <p><strong>Southern Annular Mode<br></strong>The SAM is the principal mode of variability in the atmospheric circulation of the Southern Hemisphere mid-and-high latitudes. We use the Marshall SAM Index from station-based observations of the zonal pressure difference between the latitudes of 40◦S and 65◦S.</p> <p>SAM Index was retrieved from <a href="https://climatedataguide.ucar.edu/climate-data/marshall-southern-annular-mode-sam-index-station-based">https://climatedataguide.ucar.edu/climate-data/marshall-southern-annular-mode-sam-index-station-based</a>.</p> <p>SAM dataset: <code>ds_sam.nc</code></p> <p> </p> </div>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.