{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Vertical distribution of MHWs in the cape verde region" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt \n", "import xarray as xr\n", "import cmocean.cm as cmo\n", "import scipy.signal as sig" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "plt.rc('xtick', labelsize=8)\n", "plt.rc('ytick', labelsize=8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## jobqueue" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import dask, dask_jobqueue\n", "import dask.distributed as dask_distributed" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/gxfs_work/geomar/smomw379/miniconda3/envs/py3_mhw/lib/python3.12/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", "Perhaps you already have a cluster running?\n", "Hosting the HTTP server on port 34021 instead\n", " warnings.warn(\n" ] } ], "source": [ "cluster = dask_jobqueue.SLURMCluster(\n", " # Dask worker size\n", " cores=32, memory='80GB',\n", " processes=4, # Dask workers per job\n", " # SLURM job script things\n", " queue='base', walltime='03:00:00',\n", " # Dask worker network and temporary\n", " interface='ib0', local_directory='./dask_jobqueue_logs'\n", " )\n", "\n", "client = dask_distributed.Client(cluster)\n", "cluster.scale(jobs=1)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)\n", " 17346153 base dask-wor smomw379 R 0:22 1 nesh-srp198\n" ] } ], "source": [ "!squeue -u smomw379" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Client-33a4b30c-6dfa-11f0-a87d-74563c5ee57c

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Connection method: Cluster objectCluster type: dask_jobqueue.SLURMCluster
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Cluster Info

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SLURMCluster

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abdca701

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Scheduler Info

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Scheduler

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\n", " Comm: tcp://172.18.4.21:42565\n", " \n", " Workers: 4\n", "
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Worker: SLURMCluster-0-0

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Worker: SLURMCluster-0-1

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Worker: SLURMCluster-0-2

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Worker: SLURMCluster-0-3

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" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "## load mask: host\n", "path = '/gxfs_work/geomar/smomw379/DATA/VIKING20X.L46-KFS003/'\n", "mask = xr.open_dataset(path+'atlantic_mask_3D.nc').squeeze()\n", "ATLmask = mask.tmask.isel(X=range(750,1250),z=0).rename({'X':'x','Y':'y'})\n", "ATLmask[852:,:] = 0 ## remove atlantic points outside nest domain\n", "ATLmask[0:353,:] = 0 \n", "\n", "path ='/gxfs_work/geomar/smomw355/model_data/ocean-only/ORCA025.L46-KFS006-P-V/nemo/suppl/mesh_mask.nc'\n", "dsM = xr.open_dataset(path).squeeze().isel(x=range(750,1250)).rename({'z':'deptht'})\n", "\n", "dpt = -dsM.nav_lev\n", "\n", "## grid cell area\n", "A = (dsM.e1t * dsM.e2t).where((ATLmask==1) & (dsM.tmask==1)).compute()\n", "\n", "## depth of the sea-floor in the model\n", "sfl = (dsM.e3t_0 * dsM.tmask).sum('deptht').compute()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#A = A.where(sfl>2000) ## Test whether points close to island have any impact on the results" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def area_average(dsL, dsI, dsE, Ain, zz):\n", " ## area average: weigh by grid cell area so that subpolar latitudes have no larger contribution as there are more, but smaller grid points\n", " ## average of events (ignore grid points where no heatwaves occured)\n", " A = Ain.isel(deptht=zz)\n", "\n", " Area_averageL = (dsL.where(dsL>0) * A).sum(('x','y')) / A.where(dsL>0).sum(('x','y'))\n", " Area_averageI = (dsI.where(dsI>0) * A).sum(('x','y')) / A.where(dsI>0).sum(('x','y'))\n", " Area_averageE = (dsE * A).sum(('x','y')) / A.sum(('x','y'))\n", "\n", " return Area_averageL, Area_averageI, Area_averageE" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "## cape verde region 14.5 - 17.5°N, 25.5°W - 22.5°W\n", "yCV = range(554,575)\n", "xCV = range(295,312) \n", "\n", "yCV_N = range(1002,1107) ## on nest grid \n", "xCV_N = range(1447,1532)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "time = np.arange(1980,2023)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-26.0\n", "-22.0\n", "13.862732\n", "18.661049\n" ] } ], "source": [ "print(dsM.nav_lon.isel(x=xCV,y=yCV).min().values)\n", "print(dsM.nav_lon.isel(x=xCV,y=yCV).max().values)\n", "\n", "print(dsM.nav_lat.isel(x=xCV,y=yCV).min().values)\n", "print(dsM.nav_lat.isel(x=xCV,y=yCV).max().values)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## All grid point average" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "Duration_z = np.zeros((43,46)); Intensity_z = np.zeros((43,46)); Freq_z = np.zeros((43,46)); \n", "\n", "Duration_z_lin = np.zeros((43,46)); Intensity_z_lin = np.zeros((43,46)); Freq_z_lin = np.zeros((43,46)); \n", "\n", "\n", "\n", "for zz in range(0,46):\n", " path ='/gxfs_work/geomar/smomw379/DATA/VIKING20X.L46-KFS003-6th/MHW_Detection/ANALYSIS/'\n", "\n", " if zz < 10:\n", " z = f'0{zz}'\n", " else:\n", " z=str(zz)\n", "\n", " # load clim WMO:\n", " pathC = '/gxfs_work/geomar/smomw379/DATA/VIKING20X.L46-KFS003-6th/Daily_Climatology/TMP/'\n", " dsCLIM = xr.open_mfdataset(pathC+f'VIKING20X.L46-KFS003-6th_dclim_19800101_20221231_votemper-WMO_1000-1249_500-749_{zz}.nc').isel(y=range(54,75), x=range(45,62))\n", " T_sigma = dsCLIM.sigma.where(dsCLIM.seas!=0)\n", " T_dif = (1.28*T_sigma).mean('doy')\n", "\n", "\n", " ## load data: WMO\n", " dsL_00 = xr.open_dataset(path+f'VIKING20X.L46-KFS003-6th_1y_19800101_20221231_Duration-WMO-{z}.nc').Duration_mn\n", " dsI_00 = xr.open_dataset(path+f'VIKING20X.L46-KFS003-6th_1y_19800101_20221231_IntensityMax-WMO-{z}.nc').Intensity_max_mn\n", " dsE_00 = xr.open_dataset(path+f'VIKING20X.L46-KFS003-6th_1y_19800101_20221231_Events-WMO-{z}.nc').N_events\n", "\n", " ## area average\n", " Area_averageL, Area_averageI, Area_averageE = area_average(dsL_00.isel(x=xCV,y=yCV),\n", " dsI_00.isel(x=xCV,y=yCV) / T_dif, # scaled intensity\n", " dsE_00.isel(x=xCV,y=yCV), \n", " A.isel(x=xCV,y=yCV), zz)\n", "\n", " Duration_z[:,zz] = Area_averageL.where(Area_averageL!=0)\n", " Intensity_z[:,zz] = Area_averageI.where(Area_averageI!=0)\n", " Freq_z[:,zz] = Area_averageE\n", "\n", " ## load data: detrend\n", " dsL_00 = xr.open_dataset(path+f'VIKING20X.L46-KFS003-6th_1y_19800101_20221231_Duration-detrend-{z}.nc').Duration_mn\n", " dsI_00 = xr.open_dataset(path+f'VIKING20X.L46-KFS003-6th_1y_19800101_20221231_IntensityMax-detrend-{z}.nc').Intensity_max_mn\n", " dsE_00 = xr.open_dataset(path+f'VIKING20X.L46-KFS003-6th_1y_19800101_20221231_Events-detrend-{z}.nc').N_events\n", "\n", " ## load clim: linear\n", " pathC = '/gxfs_work/geomar/smomw379/DATA/VIKING20X.L46-KFS003-6th/Daily_Climatology/TMP/'\n", " dsCLIM = xr.open_mfdataset(pathC+f'VIKING20X.L46-KFS003-6th_dclim_19800101_20221231_votemper-detrend_1000-1249_500-749_{zz}.nc').isel(y=range(54,75), x=range(45,62))\n", " T_sigma = dsCLIM.sigma.where(dsCLIM.seas!=0)\n", " T_dif = (1.28*T_sigma).mean('doy')\n", "\n", " ## area average\n", " Area_averageL_lin, Area_averageI_lin, Area_averageE_lin = area_average(dsL_00.isel(x=xCV,y=yCV),\n", " dsI_00.isel(x=xCV,y=yCV) / T_dif, # scaled intensity \n", " dsE_00.isel(x=xCV,y=yCV), \n", " A.isel(x=xCV,y=yCV), zz)\n", "\n", " Duration_z_lin[:,zz] = Area_averageL_lin.where(Area_averageL_lin!=0)\n", " Intensity_z_lin[:,zz] = Area_averageI_lin.where(Area_averageI_lin!=0)\n", " Freq_z_lin[:,zz] = Area_averageE_lin" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/gxfs_work/geomar/smomw379/miniconda3/envs/py3_mhw/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/gxfs_work/geomar/smomw379/miniconda3/envs/py3_mhw/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n" ] } ], "source": [ "## calculate explained variance by surface values \n", "Freq_cor = np.zeros(46); Freq_corD = np.zeros(46); Freq_lin_cor = np.zeros(46)\n", "Duration_cor = np.zeros(46); Duration_corD = np.zeros(46); Duration_lin_cor = np.zeros(46)\n", "Intensity_cor = np.zeros(46); Intensity_corD = np.zeros(46); Intensity_lin_cor = np.zeros(46)\n", "\n", "for z in range(0,46):\n", " Freq_cor[z] = np.corrcoef((np.nan_to_num(Freq_z.T[0,:],0), np.nan_to_num(Freq_z.T[z,:],0)))[0,1]\n", " Duration_cor[z] = np.corrcoef((np.nan_to_num(Duration_z.T[0,:],0), np.nan_to_num(Duration_z.T[z,:],0)))[0,1]\n", " Intensity_cor[z] = np.corrcoef((np.nan_to_num(Intensity_z.T[0,:],0), np.nan_to_num(Intensity_z.T[z,:],0)))[0,1]\n", "\n", " Freq_corD[z] = np.corrcoef(sig.detrend(np.nan_to_num(Freq_z.T[0,:],0)), sig.detrend(np.nan_to_num(Freq_z.T[z,:],0)))[0,1]\n", " Duration_corD[z] = np.corrcoef(sig.detrend(np.nan_to_num(Duration_z.T[0,:],0)), sig.detrend(np.nan_to_num(Duration_z.T[z,:],0)))[0,1]\n", " Intensity_corD[z] = np.corrcoef(sig.detrend(np.nan_to_num(Intensity_z.T[0,:],0)), sig.detrend(np.nan_to_num(Intensity_z.T[z,:],0)))[0,1]\n", "\n", " Freq_lin_cor[z] = np.corrcoef((np.nan_to_num(Freq_z_lin.T[0,:],0), np.nan_to_num(Freq_z_lin.T[z,:],0)))[0,1]\n", " Duration_lin_cor[z] = np.corrcoef((np.nan_to_num(Duration_z_lin.T[0,:],0), np.nan_to_num(Duration_z_lin.T[z,:],0)))[0,1]\n", " Intensity_lin_cor[z] = np.corrcoef((np.nan_to_num(Intensity_z_lin.T[0,:],0), np.nan_to_num(Intensity_z_lin.T[z,:],0)))[0,1]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(7.5,8))\n", "ax1 = fig.add_axes([0.09,0.47,0.24,0.4]); ax1.spines[['top']].set_visible(False); ax11 = fig.add_axes([0.09,0.87,0.24,0.1]); ax11.set_xticks([]); ax11.spines[['bottom']].set_visible(False)\n", "ax2 = fig.add_axes([0.38,0.47,0.24,0.4]); ax2.spines[['top']].set_visible(False); ax21 = fig.add_axes([0.38,0.87,0.24,0.1]); ax21.set_xticks([]); ax21.set_xticks([]); ax21.spines[['bottom']].set_visible(False)\n", "ax3 = fig.add_axes([0.67,0.47,0.24,0.4]); ax3.spines[['top']].set_visible(False); ax31 = fig.add_axes([0.67,0.87,0.24,0.1]); ax31.set_xticks([]); ax31.set_xticks([]); ax31.spines[['bottom']].set_visible(False)\n", "\n", "ax4 = fig.add_axes([0.09,0.06,0.24,0.42])\n", "ax5 = fig.add_axes([0.38,0.06,0.24,0.42])\n", "ax6 = fig.add_axes([0.67,0.06,0.24,0.42])\n", "\n", "##\n", "pc1 = ax1.pcolor(time, dpt, Duration_z.T, cmap='Spectral_r', vmin=0, vmax=60)\n", "ax1.set_ylim(-4000,-300)\n", "ax1.set_yticks(np.arange(-3800,-290,400))\n", "ax1.set_yticklabels(np.arange(3800,290,-400))\n", "ax1.set_ylabel('Depth [m]', fontsize=8)\n", "ax1.set_xlabel('year', fontsize=8, labelpad=2)\n", "\n", "ax11.pcolor(time, dpt, Duration_z.T, cmap='Spectral_r', vmin=0, vmax=60)\n", "ax11.set_ylim(-300,0)\n", "ax11.set_yticks(np.arange(-300,10,100))\n", "ax11.set_yticklabels(np.arange(300,-10,-100))\n", "ax11.set_title('a) Duration', loc='left', fontsize=8)\n", "\n", "cb1=plt.colorbar(pc1, orientation='horizontal', ax=[ax1,ax11], extend='both', pad=0.1)\n", "cb1.set_label('[days]', fontsize=8, x=0.98, labelpad=-30)\n", "\n", "##\n", "pc2 = ax2.pcolor(time, dpt, Freq_z.T, cmap='Spectral_r', vmin=0, vmax=3)\n", "\n", "ax2.set_ylim(-4000,-300)\n", "ax2.set_yticks(np.arange(-3800,-290,400))\n", "ax2.set_yticklabels([])\n", "ax2.set_xlabel('year', fontsize=8, labelpad=2)\n", "\n", "\n", "ax21.pcolor(time, dpt, Freq_z.T, cmap='Spectral_r', vmin=0, vmax=3)\n", "ax21.set_ylim(-300,0)\n", "ax21.set_yticks(np.arange(-300,10,100))\n", "ax21.set_yticklabels([])\n", "ax21.set_title('b) Frequency', loc='left', fontsize=8)\n", "\n", "\n", "cb2 = plt.colorbar(pc2, orientation='horizontal', ax=[ax2, ax21], extend='both', pad=0.1)\n", "cb2.set_label('[MHWs/yr]', fontsize=8, x=.9, labelpad=-30)\n", "\n", "##\n", "pc3 = ax3.pcolor(time, dpt, Intensity_z.T, cmap='Spectral_r', vmin=1, vmax=2.5)\n", "\n", "ax3.set_xlabel('year', fontsize=8, labelpad=2)\n", "ax3.set_ylim(-4000,-300)\n", "ax3.set_yticks(np.arange(-3800,-290,400))\n", "ax3.set_yticklabels([])\n", "\n", "\n", "ax31.pcolor(time, dpt, Intensity_z.T, cmap='Spectral_r', vmin=1, vmax=2.5)\n", "ax31.set_ylim(-300,0)\n", "ax31.set_yticks(np.arange(-300,10,100))\n", "ax31.set_yticklabels([])\n", "ax31.set_title('c) Maximum severity', loc='left', fontsize=8)\n", "\n", "cb3 = plt.colorbar(pc3, orientation='horizontal', ax=[ax3, ax31], extend='both', pad=0.1)\n", "\n", "##\n", "ax4.plot(Duration_cor**2*100, dpt, zorder=10, color='k', lw=1.5, label=r'Fixed$_{30yr}$')\n", "ax4.plot(Duration_corD**2*100, dpt, zorder=10, color='k', ls='--', lw=1.5, label=r'Fixed$_{30yr}$ (detr.)')\n", "ax4.plot(Duration_lin_cor**2*100, dpt, color='cornflowerblue',lw=1.5, label='Detrended')\n", "\n", "ax4.legend(fontsize=8, handlelength=1.5, handletextpad=0.2)\n", "ax4.grid(True, color='lightgrey')\n", "ax4.set_xticks(np.arange(0,105,25))\n", "ax4.set_ylim(-1500,0)\n", "ax4.set_yticks(np.arange(-1500,10,100))\n", "ax4.set_yticklabels(np.arange(1500,-10,-100))\n", "\n", "ax4.spines[['right', 'top']].set_visible(False)\n", "ax4.set_xlabel('Explained variance [%]', fontsize=8)\n", "ax4.set_ylabel('Depth [m]', fontsize=8)\n", "ax4.set_title('d) Duration', loc='left', fontsize=8)\n", "\n", "##\n", "ax5.plot(Freq_cor**2*100, dpt, color='k', lw=1.5)\n", "ax5.plot(Freq_corD**2*100, dpt, color='k', ls='--', lw=1.5)\n", "ax5.plot(Freq_lin_cor**2*100, dpt, color='cornflowerblue',lw=1.5)\n", "\n", "ax5.grid(True, color='lightgrey')\n", "ax5.set_xticks(np.arange(0,105,25))\n", "ax5.set_ylim(-1500,0)\n", "ax5.set_yticks(np.arange(-1500,10,100))\n", "ax5.set_yticklabels([]);\n", "ax5.spines[['right', 'top']].set_visible(False)\n", "ax5.set_xlabel('Explained variance [%]', fontsize=8)\n", "ax5.set_title('e) Frequency', loc='left', fontsize=8)\n", "\n", "##\n", "ax6.plot(Intensity_cor**2*100, dpt, color='k', lw=1.5)\n", "ax6.plot(Intensity_corD**2*100, dpt, color='k', ls='--', lw=1.5)\n", "ax6.plot(Intensity_lin_cor**2*100, dpt, color='cornflowerblue',lw=1.5)\n", "\n", "ax6.grid(True, color='lightgrey')\n", "ax6.set_xticks(np.arange(0,105,25))\n", "ax6.set_ylim(-1500,0)\n", "ax6.set_yticks(np.arange(-1500,10,100))\n", "ax6.set_yticklabels([]);\n", "ax6.spines[['right', 'top']].set_visible(False)\n", "ax6.set_xlabel('Explained variance [%]', fontsize=8)\n", "ax6.set_title('f) Maximum severity', loc='left', fontsize=8)\n", "\n", "plt.savefig('MHW-stats_CapeVerde.png', dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save data for publication\n", "* only store data that can not be derived from the annual mean MHW characteristics that are already stored" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "Intensity_z_lin_xr = xr.DataArray(Intensity_z_lin).rename({'dim_0':'year', 'dim_1':'deptht'})\n", "Intensity_z_xr = xr.DataArray(Intensity_z).rename({'dim_0':'year', 'dim_1':'deptht'})" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "Intensity_z_lin_xr.attrs['units'] = 'None'\n", "Intensity_z_lin_xr.attrs['long_name'] = 'Annual mean maximum severity in Cape Verde archipelago (detrended baseline)'\n", "\n", "Intensity_z_xr.attrs['units'] = 'None'\n", "Intensity_z_xr.attrs['long_name'] = 'Annual mean maximum severity in Cape Verde archipelago (fixed-30yr baseline)'" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "ds_out = xr.Dataset(data_vars={'Max_severity_lin':Intensity_z_lin_xr, 'Max_severity_WMO':Intensity_z_xr,\n", " 'year':np.arange(1980,2023), 'deptht':dsM.nav_lev})" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "## set global attributes\n", "ds_out.attrs['title'] = 'Mixed layer depth and scaled intensity for Cape Verde archipelago'\n", "ds_out.attrs['institution'] = 'GEOMAR Helmholtz Centre for Ocean Research Kiel'\n", "ds_out.attrs['creator_name'] = 'Tobias Schulzki'\n", "ds_out.attrs['creator_email'] = 'tschulzki@geomar.de'\n", "ds_out.attrs['creator_url'] = 'orcid.org/0000-0002-3480-8492'\n", "ds_out.attrs['license'] = 'CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.en)'\n", "ds_out.attrs['keywords'] = 'Temperature, marine heatwaves, VIKING20X, numerical model'\n", "ds_out.attrs['summary'] = 'Created in 9_CapeVerde.ipynb'\n", "ds_out.attrs['cdm_data_type'] = 'grid'\n", "ds_out.attrs['processing_level'] = 'Level 4 (numerical simulation output)'\n", "ds_out.attrs['source'] = 'VIKING20X'\n", "ds_out.attrs['pi'] = 'Tobias Schulzki'\n", "ds_out.attrs['pi_contact'] = 'tschulzki@geomar.de'\n", "ds_out.attrs['pi_url'] = 'orcid.org/0000-0002-3480-8492'\n", "ds_out.attrs['institution_id'] = 'https://ror.org/02h2x0161'\n", "ds_out.attrs['research_devision'] = 'Ocean Circulation and Climate Dynamics'\n", "ds_out.attrs['research_unit'] = 'Ocean Dynamics'\n", "ds_out.attrs['project'] = 'iAtlantic, METAscales'\n", "ds_out.attrs['date_created'] = '2025-07-31'\n", "ds_out.attrs['date_modified'] = '2025-07-31'\n", "ds_out.attrs['publisher_name'] = 'GEOMAR Helmholtz Centre for Ocean Research Kiel'\n", "ds_out.attrs['publisher_email'] = 'datamanagement@geomar.de'\n", "ds_out.attrs['naming_authority'] = 'de.geomar'" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "outpath = '/gxfs_work/geomar/smomw379/Publications/Schulzki2025_MHWs/DATA/'\n", "ds_out.to_netcdf(outpath+'Schulzki_et_al_2025_Figure09.nc')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "py3_std", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.0" } }, "nbformat": 4, "nbformat_minor": 2 }