Date: 24 August, 2020 Dataset Title: Factors Influencing Surface Kinetic Energy in Global High Resolution Ocean Models Dataset Creators: Jonathan Brasch, Shane Elipot, Brian Arbic Dataset Contacts: Jonathan Brasch jbrasch@umich.edu; Brian Arbic arbic@umich.edu Funding: OCE-1851164 (NSF), N00014-18-1-2544 (ONR), NNX16AH79G (NASA), NNX17AH55G, (NASA), & 80NSSC20K1135 (NASA) Key Points: - We compare kinetic energies (KE) of high-resolution global ocean models estimated from rotary spectra to KE in surface drifter observations. - Near-inertial KE is closer to drifter observations in models with frequently updated wind forcing - Internal tide KE is closer to drifter observations in models with topographic wave drag Methodology: The data are rotary spectra computed from the HYCOM and MITgcm (LLC4320) simulations subsampled to 1/4 degree. FOr each model, time series are generated based on the zonal (u) and meridional velocities (u) for a full year. These time series are split into 11 segments of 60 days each overlapping by 50%. 1D discrete Fourier transform of u + iv is computed. The Fourier coefficients are mutliplied by their complex conjugates and averaged over spegments to produce spectra. These spectra are integrated over defined frequency bands to produce kinetic energy values. Data-set information: MITgcm model: https://data.nas.nasa.gov/ecco/data.php?dir=/eccodata/llc_4320 Drifter observations: https://www.aoml.noaa.gov/phod/gdp/hourly_data.php Hycom model: HYCOM output can be accessed via the OSiRIS infrastructure, please contact for details on access. Instrument and/or Software specifications: Python 3.7.6, Matlab Files contained here: The folders show divisions based on each simulation or observation analyzed. The folders and files are described below: HYCOM: results from HYCOM subsampled to 1/4 degree Rotary Spectra: spectra_hycom_0m.nc, spectra_hycom_15m.nc X: horizontal grid spacing (1500x1) Y: vertial grid spacing (1176x1) ke_uv: spectra (1500x176x1440) freq_time: spectral frequencies (1440x1) freq_time_spacing: spacing between points in freq_time (1x1) lat: model latitude in degrees north (1500x1176) lon: model longitude in degrees east (1500x1176) Rotary Spectra averaged to 1x1 degree bins: spectra_hycom_0m_bin.nc, spectra_hycom_15m_bin.nc ke_uv_bin: spectra averaged to 1x1 degree bins (360x160x1440) freq_time: spectral frequencies (1440x1) lat: latitude range of bin-averaged data (360x1) lon: longitide range of bin-averaged data (1x160) Kinetic Energy Results: ke_hycom_15m.mat, ke_hycom_0m.mat lat_grid: model latitude averaged to 1x1 degree bins (360x160) lon_grid: model longitide averaged to 1x1 degree bins (360x160) xmid: latitude at bin centers (160x1) ymid: longitide at bin centers (360x1) For each energy band: [energy-band]: ke results on model grid (1500x1176) [energy-band]_bin: ke results averaged to 1x1 degree bins (360x160) [energy-band]_avg: ke results zonally averaged from 1x1 degree bins (160x1) Grid Data: topo.mat plat: latitude in degrees north (9000x7055) plon: longitude in degrees east (9000x7055) depth: ocean depth in meters (9000x7055) MITgcm: results from MITgcm subsampled to 1/4 degree Rotary Spectra: spectra_llc4320_0m.nc, spectra_llc4320_15m.nc i: horizontal grid spacing (1440x1) j: vertial grid spacing (1080x1) ke_uv: spectra (1440x1080x1440x1) freq_time: spectral frequencies (1440x1) freq_time_spacing: spacing between points in freq_time (1x1) Rotary Spectra averaged to 1x1 degree bins: spectra_llc4320_0m_bin.nc, spectra_llc4320_15m_bin.nc ke_uv_bin: spectra averaged to 1x1 degree bins (360x162x1440) freq_time: spectral frequencies (1440x1) lat: latitude range of bin-averaged data (360x1) lon: longitide range of bin-averaged data (1x162) Kinetic Energy Results: ke_llc4320_0m.mat, ke_llc4320_15m.mat lat_grid: model latitude averaged to 1x1 degree bins (360x162) lon_grid: model longitide averaged to 1x1 degree bins (360x162) xmid: latitude at bin centers (162x1) ymid: longitide at bin centers (360x1) For each energy band: [energy-band]: ke results on model grid (1440x1080) [energy-band]_bin: ke results averaged to 1x1 degree bins (360x162) [energy-band]_avg: ke results zonally averaged from 1x1 degree bins (162x1) Grid Data: XC.nc XC: longitude East of center of grid cell (1440x1080) YC.nc YC: latitude North of center of grid cell Depth.nc Depth: ocean depth in meters (all grid files contain same i and j as MITgcm spectra files in addition to their grid variable) Drifters: (more information on how this folder is organized is found at a readme within the folder) Kinetic Energy Results (bin averaged): ke_drifters.mat mz*: mean kinetic energy values on 1 degree by 1 degree bins numz*: number of elements to form averages in each bin (168x360) xmid*: longitide at bin centers (360x1) ymid*: latitude at bin centers (168x1) Kinetic Energy Results (zonally averaged): ke_drifters_avg.mat ymid: latitude at bin centers (168x1) xmid: longitide at bin centers (360x1) For each energy band: [energy-band]_[drouged|undrouged]: ke results averaged to 1x1 degree bins (168x360) [energy-band]_[drouged|undrouged]_avg: ke results zonally averaged from 1x1 degree bins (168x1) Rotary Spectra averaged to 1x1 degree bins, zonally averaged: spectra_drifters.mat msnn: negative spectra for all drifters (120x721) mspp: positive spectra for all drifters (120x721) msnn_d: negative spectra for drogued drifters (120x721) mspp_d: positive spectra for drogued drifters (120x721) msnn_u: negative spectra for undrogued drifters (120x721) mspp_u: positive spectra for undrogued drifters (120x721) ymid: latitude at bin centers (120x1) xmid: frequency at bin centers (721x1) spectra: code to produce spectra hycom: hycom.py: Create zarrs, subsample model, compute rotary spectra & total KE spectra_bin.m: average spectra to 1x1 degree bins mitgcm: subsample.ipynb: Subsample model concat_rechunk.py: Concat and rechunk subsampled timeseries spectra.ipynb: compute rotary spectra & total KE spectra_bin.m: average spectra to 1x1 degree bins ke: code to produce kinetic energy results from spectra [hycom|mitgcm|drifter]_ke.m: organize integration, bin-average, shift grid [hycom|mitgcm]_shift_grid.m: shift grid for easy mapping ke_integrate.m: integrate frequency bands ke_integrate_wide.m: integrate wide frequency bands figures: code to produce figures FIG_freq_lat.ipynb: visualize zonally averaged spectra FIG_zonal_avgs.m: visualize zonally averaged kinetic energy for the frequency bands FIG_maps.m: visualize kinetic energy for the frequency bands. Related publication(s): Brasch, J.M., et al. (2020). Factors Influencing Surface Kinetic Energy in Global High Resolution Ocean Models. Forthcoming. Use and Access: This data set is made available under a Attribution 4.0 International (CC BY 4.0). To Cite Data: Brasch, J.M., et al. (2020). Factors Influencing Surface Kinetic Energy in Global High Resolution Ocean Models [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/r8q1-g224