{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# North Atlantic Subpolar Gyre Index from EOF analysis\n",
    "### for VIKING20X paper\n",
    "\n",
    "Use SSH to compute PC1 and PC 2 following Hatun and Chafik (2018) as well as Koul et al. (2020)\n",
    "\n",
    "Defining an SPG index based on SSH EOF analysis:\n",
    "\"The first index (PC1 SSH) is defined as the principal component of the leading Empirical Orthogonal Function (EOF) of annual mean SSH anomalies in the subpolar North Atlantic, defined in the domain 20°N to 70°N, 0°W to 80°W (see Hakkinen and Rhines, 2004), and defined for the altimeter period 1993–2016. Similarly, the second index (PC2 SSH) is defined as the principal component of the second EOF of annual mean SSH anomalies.\" (Koul et al., 2020)\n",
    "\n",
    "**The subpolar North Atlantic is defined as the region 20-70N, 0-80W** (Koul et al., 2020).\n",
    "In fact, Hakkinen and Rhines used data that did not extend north of about 65N and not into the Hudson Bay. (cf H&R04 as well as Hatun and Chafik, 2018)\n",
    "\n",
    "[EOF-software by Andrew Dawson](https://ajdawson.github.io/eofs/latest/):\n",
    "Dawson, A., 2016. eofs: A Library for EOF Analysis of Meteorological, Oceanographic, and Climate Data. Journal of Open Research Software, 4(1), p.e14. DOI: http://doi.org/10.5334/jors.122\n",
    "\n",
    "Monthly SPG index of H&C18 at https://bolin.su.se/data/chafik-2019-3\n",
    "\n",
    "Time series of the subpolar gyre index in \\Oj (thin black), \\Vjs (thick black dotted), \\Vjl (thick black solid) and \\Vc (blue), and based on observations (orange).\n",
    "\n",
    "Also provide mean and std.dev for 1990-2009 and correlations between time series\n",
    "\n",
    "Vielleicht macht die Korrelation sowohl auf kurzen (1990-2009, dann mit Beobachtungen) als auch langen (1958-2009, dann nur Modelle; ohne die Trends werden die auch gut korreliert sein) Sinn. Letztendlich möchte ich damit zwei Aussagen dokumentieren: Die Modelle stimmen hinsichtlich der interannualen Variabilität gut mit den Beobachtungen überein. Die dekadische Variabilität ist über die einzelnen Experimente relativ robust. Beides dokumentiert die Wichtigkeit des Windantriebs. \n",
    "\n",
    "### Model runs\n",
    "| Model | pen style | data path |\n",
    "| --- | --- | --- |\n",
    "| OJo (ORCA025-JRA-OMIP) | solid thin red | scalc01:/data/user/tomartin/Models/NEMO/orca025.l46/experiments/ORCA025.L46-KFS003-V/derived |\n",
    "| OJo2 (ORCA025-JRA-OMIP-2nd) | dashed thin red | scalc01:/data/user/tomartin/Models/NEMO/orca025.l46/experiments/ORCA025.L46-KFS003-V-2nd/derived |\n",
    "| OJ (ORCA025-JRA) | solid thin green | nesh-fe:/sfs/fs1/work-geomar3/smomw091/SDIR/ORCA025.L46/ORCA025.L46-KFS001-V/1m/ |\n",
    "| OJst (ORCA025-JRA-strong) | dashed thin green | blogin:/scratch/usr/shkifmfs/shared/ORCA025.L46-KFS006_monthly_SSH |\n",
    "| VJo (VIKING20X-JRA-OMIP) | solid red | scalc01:/data/user/tomartin/Models/NEMO/viking20x.l46/experiments/VIKING20X.L46-KFS003/derived |\n",
    "| VJl (VIKING20X-JRA-long) | solid blue | nesh-fe:/sfs/fs1/work-geomar3/smomw091/SDIR/VIKING20X.L46/VIKING20X.L46-KFS001-S/1m/ |\n",
    "| VJs (VIKING20X-JRA-short) | dashed blue | nesh-fe:/sfs/fs1/work-geomar3/smomw091/SDIR/VIKING20X.L46/VIKING20X.L46-KKG36107B-S/1m/ |\n",
    "| VC (VIKING20X-CORE) | solid black | nesh-fe:/sfs/fs1/work-geomar3/smomw091/SDIR/VIKING20X.L46/VIKING20X.L46-KKG36013H-S/1m/ |\n",
    "| Observations | solid orange | scalc01:/data/user/tomartin/Observations/SSH/ |\n",
    "\n",
    "### AVISO+ observations\n",
    "\n",
    "adt:long_name = \"Absolute dynamic topography\" ;\n",
    "adt:standard_name = \"sea_surface_height_above_geoid\" ;\n",
    "adt:units = \"m\" ;\n",
    "adt:comment = \"The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details\" ;\n",
    "\n",
    "sla:long_name = \"Sea level anomaly\" ;\n",
    "sla:standard_name = \"sea_surface_height_above_sea_level\" ;\n",
    "sla:units = \"m\" ;\n",
    "sla:comment = \"The sea level anomaly is the sea surface height above mean sea surface; it is referenced to the [1993, 2012] period; see the product user manual for details\" ;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initalization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from eofs.xarray import Eof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_figure=True\n",
    "\n",
    "projname='viking20x_paper_spg_index_eof'\n",
    "projpath='../data/data_figure10'\n",
    "\n",
    "aviso_varname='adt' # adt or sla where adt is the better match for NEMO's diagnosed sossheig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### KG\n",
    "TINY1_SIZE=16\n",
    "TINY2_SIZE=20\n",
    "###\n",
    "SMALL_SIZE=18\n",
    "MED_SIZE=24\n",
    "BIG_SIZE=28\n",
    "plt.rc('font',   size     =SMALL_SIZE)    # controls default text sizes\n",
    "### plt.rc('axes',   titlesize=SMALL_SIZE)    # fontsize of the axes title\n",
    "### plt.rc('axes',   labelsize=MED_SIZE)      # fontsize of the x and y labels\n",
    "plt.rc('axes',   titlesize=SMALL_SIZE)    # fontsize of the axes title\n",
    "plt.rc('axes',   labelsize=SMALL_SIZE)      # fontsize of the x and y labels\n",
    "plt.rc('xtick',  labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick',  labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize =SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIG_SIZE)      # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#workdir='/data/user/tomartin/Models/NEMO/' # GEOMAR\n",
    "#obspath='/data/user/tomartin/Observations/SSH/MonthlyMean/dt_global_allsat_phy_l4_mm_1993-2018_'+aviso_varname+'.nc'\n",
    "\n",
    "shortname=['OJo','OJo2','Oj','Ojst',\n",
    "           'VJo','Vjl','Vjs','Vc','Obs']\n",
    "longname=['ORCA025-JRA-OMIP','ORCA025-JRA-OMIP-2nd','ORCA025-JRA','ORCA025-JRA-strong',\n",
    "          'VIKING20X-JRA-OMIP','VIKING20X-JRA-long','VIKING20X-JRA-short','VIKING20X-CORE','Observations']\n",
    "modelname=['orca025.l46','orca025.l46','orca025.l46','orca025.l46',\n",
    "           'viking20x.l46','viking20x.l46','viking20x.l46','viking20x.l46','aviso.']\n",
    "expname=['ORCA025.L46-KFS003-V','ORCA025.L46-KFS003-V-2nd','ORCA025.L46-KFS001-V','ORCA025.L46-KFS006',\n",
    "         'VIKING20X.L46-KFS003','VIKING20X.L46-KFS001','VIKING20X.L46-KKG36107B','VIKING20X.L46-KKG36013H','AVISO']\n",
    "year1=['1958','1958','1958','1958',\n",
    "       '1958','1958','1980','1958','1993']\n",
    "year2=['2019','2019','2019','2019',\n",
    "       '2019','2019','2019','2009','2018']\n",
    "\n",
    "penwid=[1,1,1,1,3,3,3,3,3]\n",
    "pensty=['-','--','-','--','-','-','--','-','-']\n",
    "dshsty=[[1,0],[6,4],[1,0],[6,4],[1,0],[1,0],[6,4],[1,0],[1,0]]\n",
    "\n",
    "### pencol=['r','r',(0,.6,0),(0,.6,0),'r','b','b','k',(1,.7,0)]\n",
    "### pencol=['r','r',(0,.6,0),(0,.6,0),'r','b','b','k',(1., 0.5, 0, 1)]\n",
    "col_O025str = (0, 0.6, 0, 1);  # darkgreen\n",
    "col_OBSstr  = (1., 0.5, 0, 1); # orange\n",
    "pencol=['r','r',col_O025str,col_O025str,'r','b','b','k',col_OBSstr]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cellarea(gridname):\n",
    "    \"\"\"\n",
    "    returns grid-cell areas computed from e1t and e2t\n",
    "    \"\"\"\n",
    "    gridname=gridname.lower()\n",
    "    if gridname == 'orca05':\n",
    "        meshfile='/home/tomartin/ModelGrids/NEMO-ORCA05/ORCA05.L46_mesh_mask.nc'\n",
    "    elif gridname == 'orca025':\n",
    "        meshfile='/home/tomartin/ModelGrids/NEMO-ORCA025/mesh_hgr.nc'\n",
    "    elif gridname == 'viking20x':\n",
    "        meshfile='/home/tomartin/ModelGrids/NEMO-VIKING20X/mesh_mask.nc'\n",
    "    elif gridname == '1_viking20x':\n",
    "        meshfile='/home/tomartin/ModelGrids/NEMO-VIKING20X/1_mesh_mask.nc'\n",
    "    else:\n",
    "        print('ERROR in get_cellarea: gridname',gridname,'not implemented')\n",
    "        return\n",
    "    ds=xr.open_dataset(meshfile)\n",
    "    area=(ds.e1t*ds.e2t).squeeze()\n",
    "    return area.values\n",
    "\n",
    "def get_tmask0(gridname):\n",
    "    \"\"\"\n",
    "    returns land-sea mask for surface layer of T-grid\n",
    "    \"\"\"\n",
    "    gridname=gridname.lower()\n",
    "    if gridname == 'orca05':\n",
    "        meshfile='/home/tomartin/ModelGrids/NEMO-ORCA05/ORCA05.L46_mesh_mask.nc'\n",
    "    elif gridname == 'orca025':\n",
    "        meshfile='/home/tomartin/ModelGrids/NEMO-ORCA025/mask.nc'\n",
    "    elif gridname == 'viking20x':\n",
    "        meshfile='/home/tomartin/ModelGrids/NEMO-VIKING20X/mesh_mask.nc'\n",
    "    elif gridname == '1_viking20x':\n",
    "        meshfile='/home/tomartin/ModelGrids/NEMO-VIKING20X/1_mesh_mask.nc'\n",
    "    else:\n",
    "        print('ERROR in get_cellarea: gridname',gridname,'not implemented')\n",
    "        return\n",
    "    ds=xr.open_dataset(meshfile)\n",
    "    tmask0=ds.tmask.isel(z=0)\n",
    "    tmask0=tmask0.where(tmask0>0).squeeze()\n",
    "    return tmask0.values\n",
    "\n",
    "def get_ssh_glbm(dataset,gridname):\n",
    "    \"\"\"\n",
    "    returns global mean SSH using grid-cell area weighted averaging\n",
    "    \"\"\"\n",
    "#    gridname=dataset.name[dataset.name.rfind('/')+1:dataset.name.find('.L46')].lower()\n",
    "    gridname=gridname.lower()\n",
    "    mask=get_tmask0(gridname)\n",
    "    area=get_cellarea(gridname)*mask\n",
    "    ssh=(dataset.sossheig*area).sum(('y','x'),skipna=True)/np.nansum(area)\n",
    "    try:\n",
    "        ssh=shh.reset_coords('time_centered',drop=True)\n",
    "        print('time_centered removed')\n",
    "    except:\n",
    "        print('no time_centered found')\n",
    "    return ssh #.values\n",
    "\n",
    "def get_ssh_spg(dataset,gridname):\n",
    "    \"\"\"\n",
    "    returns the mean SSH at (57˚N,52˚W) corrected by the global mean SSH\n",
    "      a spatial mean over a box of approx. 2˚x2˚ is computed\n",
    "      using grid-cell area weighted averaging\n",
    "    \"\"\"\n",
    "    # (i,j) locations of (57N,52W)\n",
    "#    gridname=dataset.name[dataset.name.rfind('/')+1:dataset.name.find('.L46')].lower()\n",
    "    gridname=gridname.lower()\n",
    "    if gridname == 'orca05':\n",
    "        j57n=388; i52w=475; nspan=2; jfac=2 # LAT 57.03027 LON -51.841125\n",
    "    elif (gridname == 'orca025') or (gridname == 'viking20x'):\n",
    "        j57n=776; i52w=950; nspan=4; jfac=2 # LAT 57.03027 LON -51.841125\n",
    "    elif gridname == '1_viking20x':\n",
    "        j57n=np.nan\n",
    "        i52w=np.nan\n",
    "        nspan=np.nan\n",
    "    elif gridname == 'aviso':\n",
    "        j57n=588; i52w=1232; nspan=4; jfac=1 # LAT 57.125 LON -51.875\n",
    "    else:\n",
    "        print('ERROR in get_ssh_spg: gridname',gridname,'not implemented')\n",
    "        return\n",
    "    jslice=slice(j57n-nspan*jfac,j57n+nspan*jfac+1)\n",
    "    islice=slice(i52w-nspan,i52w+nspan+1)\n",
    "    #\n",
    "    if gridname != 'aviso':\n",
    "        # get masked grid-cell area\n",
    "        mask=get_tmask0(gridname)[jslice,islice]\n",
    "        area=get_cellarea(gridname)[jslice,islice]*mask\n",
    "        # get global mean SSH\n",
    "        ssh_glbm=get_ssh_glbm(dataset,gridname)\n",
    "        # compute SSH at SPG center\n",
    "        ssh=(dataset.sossheig.isel(y=jslice,x=islice).squeeze()*area).sum(('y','x'),skipna=True)/np.nansum(area)\n",
    "    else:\n",
    "        # get masked grid-cell area\n",
    "        mask=get_mask_aviso()[jslice,islice]\n",
    "        area=get_cellarea_aviso()[jslice,islice]*mask\n",
    "        # get global mean SSH\n",
    "        ssh_glbm=get_ssh_glbm_aviso(dataset)\n",
    "        # compute SSH at SPG center\n",
    "        ssh=eval('(dataset.'+aviso_varname+'.isel(latitude=jslice,longitude=islice).squeeze()*area).sum((\\'latitude\\',\\'longitude\\'),skipna=True)/np.nansum(area)')\n",
    "    ssh=ssh-ssh_glbm.values\n",
    "    return ssh #.values\n",
    "\n",
    "def get_ssh_NA(dataset,gridname):\n",
    "    \"\"\"\n",
    "    returns a cropped SSH array with only the region 30-70N, 80W-10E\n",
    "    and grid cell areas for same region\n",
    "    \"\"\"\n",
    "    gridname=gridname.lower()\n",
    "    if gridname == 'orca05':\n",
    "        yslice=slice(289,430)\n",
    "        xslice=slice(412,574)\n",
    "    elif gridname == 'orca025':\n",
    "        yslice=slice(578,860)\n",
    "        xslice=slice(824,1148)\n",
    "    elif gridname == 'viking20x':\n",
    "        yslice=slice(578,860)\n",
    "        xslice=slice(824,1148)\n",
    "    elif gridname == '1_viking20x':\n",
    "        print('ERROR: subdomain for EOF not yet specified')\n",
    "        return\n",
    "    elif gridname == 'aviso':\n",
    "        xslice=slice(1120,1440)\n",
    "        yslice=slice(440,640)    #\n",
    "    if gridname != 'aviso':\n",
    "        mask=get_tmask0(gridname); mask=mask[yslice,xslice]\n",
    "        area=get_cellarea(gridname); area=area[yslice,xslice]\n",
    "        ssh=dataset.sossheig.isel(y=yslice,x=xslice)*mask\n",
    "        try:\n",
    "            ssh=shh.reset_coords('time_centered',drop=True)\n",
    "            print('time_centered removed')\n",
    "        except:\n",
    "            print('no time_centered found')\n",
    "    else:\n",
    "        mask=get_mask_aviso()[yslice,xslice]\n",
    "        area=get_cellarea_aviso()[yslice,xslice]*mask\n",
    "        ssh=eval('dataset.'+aviso_varname+'.isel(latitude=yslice,longitude=xslice).squeeze()')\n",
    "    #\n",
    "    return ssh,area #.values\n",
    "\n",
    "def get_cellarea_aviso():\n",
    "    \"\"\"\n",
    "    Read grid-cell area for AVISO data\n",
    "    computed with cdo gridarea\n",
    "    \"\"\"\n",
    "    area=xr.open_dataset('/data/user/tomartin/Observations/SSH/gridcellarea.nc').cell_area.squeeze().values\n",
    "    return area\n",
    "\n",
    "def get_mask_aviso():\n",
    "    \"\"\"\n",
    "    Derive land-sea mask from data\n",
    "    \"\"\"\n",
    "    ssh=eval('xr.open_dataset(obspath,decode_times=False).'+aviso_varname+'.sum(\\'time\\').squeeze()')\n",
    "    mask=ssh*0.0+1.0\n",
    "    return mask.values\n",
    "\n",
    "def get_ssh_glbm_aviso(dataset):\n",
    "    mask=get_mask_aviso()\n",
    "    area=get_cellarea_aviso()*mask\n",
    "    ssh=eval('(dataset.'+aviso_varname+'*area).sum((\\'latitude\\',\\'longitude\\'),skipna=True)/np.nansum(area)')\n",
    "    return ssh #.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SPG index plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "use_ym=True   # compute annual means\n",
    "use_ts=True   # use already computed timeseries (False for reading from original data)\n",
    "\n",
    "eof_n=2\n",
    "eof_sign=[1,-1,-1,1,-1,-1,-1,1,1] # set sign of PC to align all data sets (EOF analysis may yield mirrored patterns)\n",
    "imode=1\n",
    "\n",
    "ref_period=(1990,2009)\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(12,5))\n",
    "\n",
    "#for irun in range(0,9):\n",
    "for irun in np.array([7,6,5,4,0,8]):\n",
    "#    print('irun=',irun)\n",
    "    if use_ts:\n",
    "\n",
    "        npzfile = np.load('../data/data_figure10/'+projname+'_ts_'+str(irun)+'.npz')\n",
    "        ssh_pc=npzfile['pc']\n",
    "        time=npzfile['time']\n",
    "    else:\n",
    "        gridname=modelname[irun][0:modelname[irun].find('.')]\n",
    "        if irun == len(shortname)-1:\n",
    "            inpath=obspath\n",
    "            ds=xr.open_dataset(inpath)\n",
    "            ssh_gm_ltm=get_ssh_glbm_aviso(ds).mean()\n",
    "        else:\n",
    "            inpath=workdir+modelname[irun]+'/experiments/'+expname[irun]+'/derived/'+expname[irun]+'_1m_'+year1[irun]+'0101_'+year2[irun]+'1231_sossheig.nc'\n",
    "            ds=xr.open_dataset(inpath).rename({'time_counter':'time'})\n",
    "            ssh_gm_ltm=get_ssh_glbm(ds,gridname).mean()\n",
    "        ssh,wgts=get_ssh_NA(ds,gridname)\n",
    "        wgts=wgts/np.sum(wgts)\n",
    "        if use_ym:\n",
    "            ssh=ssh.groupby('time.year').mean('time')\n",
    "            time=np.arange(int(year1[irun]),int(year2[irun])+1,1)\n",
    "        else:\n",
    "            time=np.arange(int(year1[irun]),int(year2[irun])+1,1/12)\n",
    "        data=(ssh-ssh_gm_ltm).rename({'year':'time'})\n",
    "        # EOF:\n",
    "        eof_solver = Eof(data, weights=wgts)\n",
    "        ssh_eof = eof_solver.eofs(neofs=eof_n)\n",
    "        ssh_pc  = eof_solver.pcs(npcs=eof_n,pcscaling=1) # scaled to unit variance\n",
    "        ssh_var = eof_solver.varianceFraction(neigs=eof_n)*100 # in %\n",
    "        # save result:\n",
    "        np.savez('../data/data_figure10/tmp/'+projname+'_ts_'+str(irun)+'.npz',eof=ssh_eof,pc=ssh_pc,var=ssh_var,time=time)\n",
    "        ssh_pc=ssh_pc.values\n",
    "    #            \n",
    "    ssh_spg=ssh_pc[:,imode] # 2nd mode is SPG index according to Koul et al\n",
    "    #\n",
    "    ax.plot(time,ssh_spg*eof_sign[irun],\n",
    "        color=pencol[irun],linestyle=pensty[irun],dashes=dshsty[irun],linewidth=penwid[irun],\n",
    "        label=longname[irun])\n",
    "\n",
    "    if irun == 0:\n",
    "        ax.plot(time,ssh_spg*0.0, color='k',linestyle='-',linewidth=0.5)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.02, 1.), loc='upper left',\n",
    "    ncol=1, borderaxespad=0., fontsize=14, handlelength=2.9)#, mode=\"expand\")\n",
    "\n",
    "# plt.xlabel('year')\n",
    "plt.ylabel('standardized SSH index')\n",
    "# plt.ylabel('standardized SSH index', fontsize=14)\n",
    "plt.xlim([1958,2020])\n",
    "plt.ylim([-3,3])\n",
    "\n",
    "if save_figure:\n",
    "    plt.savefig('./figure10_revised.png',dpi=300,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compute correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlationsfor 1993-2009\n",
      "Observations with ORCA025-JRA-OMIP    0.86 at signif.level  1.00\n",
      "Observations with VIKING20X-JRA-OMIP  0.71 at signif.level  1.00\n",
      "Observations with VIKING20X-JRA-long  0.76 at signif.level  1.00\n",
      "Observations with VIKING20X-JRA-short  0.34 at signif.level  0.80\n",
      "Observations with VIKING20X-CORE      0.85 at signif.level  1.00\n",
      "\n",
      "correlations for 1958-2009\n",
      "VIKING20X-CORE with ORCA025-JRA-OMIP    0.05 at signif.level  0.26\n",
      "VIKING20X-CORE with ORCA025-JRA-OMIP-2nd  0.48 at signif.level  1.00\n",
      "VIKING20X-CORE with ORCA025-JRA         0.52 at signif.level  1.00\n",
      "VIKING20X-CORE with ORCA025-JRA-strong -0.18 at signif.level  0.78\n",
      "VIKING20X-CORE with VIKING20X-JRA-OMIP  0.14 at signif.level  0.69\n",
      "VIKING20X-CORE with VIKING20X-JRA-long  0.09 at signif.level  0.47\n"
     ]
    }
   ],
   "source": [
    "print('correlationsfor 1993-2009')\n",
    "ref_period=(1993,2009)\n",
    "# observations as reference:\n",
    "for i,irun in enumerate((8,0,4,5,6,7)):\n",
    "    npzfile = np.load('../data/data_figure10/'+projname+'_ts_'+str(irun)+'.npz')\n",
    "    ssh_pc=npzfile['pc']\n",
    "    ssh_spg=ssh_pc[:,imode]\n",
    "    time=npzfile['time']\n",
    "    t1=np.min(np.where(time>=ref_period[0]))\n",
    "    t2=np.max(np.where(time<=ref_period[1]))\n",
    "    if i == 0:\n",
    "        ref=ssh_spg[t1:t2]*eof_sign[irun]\n",
    "    else:\n",
    "        exp=ssh_spg[t1:t2]*eof_sign[irun]\n",
    "        r,p=stats.pearsonr(ref,exp)\n",
    "        print('Observations with','{:<18}'.format(longname[irun]),'{:5.2f}'.format(r),'at signif.level','{:5.2f}'.format(1-p))\n",
    "        \n",
    "        \n",
    "print('')\n",
    "print('correlations for 1958-2009')\n",
    "ref_period=(1958,2009)\n",
    "# ORCA025-JRA as reference:\n",
    "for i,irun in enumerate((7,0,1,2,3,4,5)):\n",
    "    npzfile = np.load('../data/data_figure10/'+projname+'_ts_'+str(irun)+'.npz')\n",
    "    ssh_pc=npzfile['pc']\n",
    "    ssh_spg=ssh_pc[:,imode]\n",
    "    time=npzfile['time']\n",
    "    t1=np.min(np.where(time>=ref_period[0]))\n",
    "    t2=np.max(np.where(time<=ref_period[1]))\n",
    "    if i == 0:\n",
    "        ref=ssh_spg[t1:t2]*eof_sign[irun]\n",
    "        refname=longname[irun]\n",
    "    else:\n",
    "        exp=ssh_spg[t1:t2]*eof_sign[irun]\n",
    "        r,p=stats.pearsonr(ref,exp)\n",
    "        print('{:<12}'.format(refname),'with','{:<18}'.format(longname[irun]),'{:5.2f}'.format(r),\n",
    "              'at signif.level','{:5.2f}'.format(1-p))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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