{
 "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": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_figure=True\n",
    "\n",
    "projname='viking20x_paper_spg_index_eof'\n",
    "projpath='../data/data_figure10'\n",
    "figurefile='./figure10_revised2'\n",
    "\n",
    "aviso_varname='adt' # adt or sla where adt is the better match for NEMO's diagnosed sossheig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "SMALL_SIZE=22\n",
    "MED_SIZE=26\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": 6,
   "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": 7,
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 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=(12,7))\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(projpath+projname+'_ts_'+str(irun)+'.npz')\n",
    "\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(projpath+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",
    "# legend outside plot:\n",
    "#ax.legend(bbox_to_anchor=(1.02, 1.), loc='upper left', ncol=1, \n",
    "#    borderaxespad=0., fontsize=14, handlelength=2.9)#, mode=\"expand\")\n",
    "# legend inside plot:\n",
    "ax.legend(loc='lower center', ncol=3, \n",
    "    borderaxespad=0., fontsize=15, 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(figurefile+'.pdf',bbox_inches='tight')\n",
    "    plt.savefig(figurefile+'.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(projpath+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(projpath+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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