{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Code for Fig. 4, fig. S4, and Table 1 of\n", "#### Yeager et al., 2021: An Outsized Role for the Labrador Sea in the Multidecadal Variability of the Atlantic Overturning Circulation, *Science Advances*." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import xarray as xr \n", "import numpy as np \n", "import cftime\n", "import copy\n", "import scipy.stats\n", "from scipy import signal,stats\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def open_POPdataset(x):\n", " ds = xr.open_dataset(x,decode_times=False)\n", " attrs=ds.time.attrs.copy()\n", " ds = ds.assign_coords(time=ds.time.values - 15)\n", " ds.time.attrs = attrs\n", " ds = xr.decode_cf(ds)\n", " return ds" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "datadir = '/glade/scratch/yeager/YeagerEA_ScienceAdvances_2021/'\n", "f2_hr = f'{datadir}/B.E.13.B1850C5.ne120_t12.sehires38.003.sunway_02.pop.h.020001_050012.MOCsig.nc'\n", "f3_hr = f'{datadir}/B.E.13.B1850C5.ne120_t12.sehires38.003.sunway_02.pop.h.020001_050012.WMF.nc'\n", "ds2_hr = open_POPdataset(f2_hr) \n", "ds3_hr = open_POPdataset(f3_hr) " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Add combined regions to WMF datasets\n", "ds3_hr1 = ds3_hr.isel(wmf_region=[1,2,3,5,6]).sum('wmf_region')\n", "ds3_hr1 = ds3_hr1.assign_coords({'wmf_region':'ALL minus LAB (7)'})\n", "ds3_hr2 = ds3_hr.isel(wmf_region=[1,2,3]).sum('wmf_region')\n", "ds3_hr2 = ds3_hr2.assign_coords({'wmf_region':'IRM+SPG (8)'})\n", "ds3_hr3 = ds3_hr.isel(wmf_region=[1,4]).sum('wmf_region')\n", "ds3_hr3 = ds3_hr3.assign_coords({'wmf_region':'LAB+SPG-west (9)'})\n", "ds3_hr4 = ds3_hr.isel(wmf_region=[2,3]).sum('wmf_region')\n", "ds3_hr4 = ds3_hr4.assign_coords({'wmf_region':'IRM+SPG-east (10)'})\n", "ds3_hr5 = ds3_hr.isel(wmf_region=[2,3,5,6]).sum('wmf_region')\n", "ds3_hr5 = ds3_hr5.assign_coords({'wmf_region':'ALL minus LAB+SPG-west (11)'})\n", "ds3_hr = xr.concat([ds3_hr,ds3_hr1,ds3_hr2,ds3_hr3,ds3_hr4,ds3_hr5],dim='wmf_region')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'wmf_region' (wmf_region: 12)>\n",
       "array(['All (>0)', 'SPG_west (1)', 'SPG_east (2)', 'Irminger Sea (3)',\n",
       "       'Labrador Sea (4)', 'Norwegian Sea (5)', 'Arctic (6)',\n",
       "       'ALL minus LAB (7)', 'IRM+SPG (8)', 'LAB+SPG-west (9)',\n",
       "       'IRM+SPG-east (10)', 'ALL minus LAB+SPG-west (11)'], dtype=object)\n",
       "Coordinates:\n",
       "  * wmf_region  (wmf_region) object 'All (>0)' ... 'ALL minus LAB+SPG-west (11)'
" ], "text/plain": [ "\n", "array(['All (>0)', 'SPG_west (1)', 'SPG_east (2)', 'Irminger Sea (3)',\n", " 'Labrador Sea (4)', 'Norwegian Sea (5)', 'Arctic (6)',\n", " 'ALL minus LAB (7)', 'IRM+SPG (8)', 'LAB+SPG-west (9)',\n", " 'IRM+SPG-east (10)', 'ALL minus LAB+SPG-west (11)'], dtype=object)\n", "Coordinates:\n", " * wmf_region (wmf_region) object 'All (>0)' ... 'ALL minus LAB+SPG-west (11)'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds3_hr.wmf_region" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute Annual Anomalies and Std Dev (years 200-500)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ds2_hr_ann = ds2_hr.groupby('time.year').mean('time')\n", "ds2_hr_ann=ds2_hr_ann.rename({'year':'time'}).sel(time=slice(200,501))\n", "ds3_hr_ann = ds3_hr.groupby('time.year').mean('time')\n", "ds3_hr_ann=ds3_hr_ann.rename({'year':'time'}).sel(time=slice(200,501))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "ds2_hr_mean = ds2_hr_ann.mean('time')\n", "ds2_hr_annanom = ds2_hr_ann - ds2_hr_mean\n", "ds3_hr_mean = ds3_hr_ann.mean('time')\n", "ds3_hr_annanom = ds3_hr_ann - ds3_hr_mean" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Annual Detrended Anomalies\n", "ds2_hr_annanom_dt = xr.apply_ufunc(signal.detrend, ds2_hr_annanom.fillna(0), kwargs={'axis': 0}).where(ds2_hr_annanom.notnull())\n", "ds3_hr_annanom_dt = xr.apply_ufunc(signal.detrend, ds3_hr_annanom.fillna(0), kwargs={'axis': 0}).where(ds3_hr_annanom.notnull())\n", "ds2_hr_ann_dt = ds2_hr_annanom_dt + ds2_hr_mean\n", "ds3_hr_ann_dt = ds3_hr_annanom_dt + ds3_hr_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Temporal Filtering" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cutoff= 10.0 years\n" ] } ], "source": [ "# 10-year butterworth low-pass filter\n", "fs=1/(365*24*3600) # 1 year in Hz (sampling frequency)\n", "nyquist = fs / 2 # 0.5 times the sampling frequency\n", "cutoff = fs/10 # 10-year cutoff frequency\n", "cutoff = cutoff/nyquist # as fraction of nyquist \n", "print('cutoff= ',(1/(cutoff*nyquist))/(365*24*3600),' years') \n", "filtsos = signal.butter(4, cutoff, 'lowpass', output='sos') #low pass filter\n", "filtb, filta = signal.butter(4, cutoff, 'lowpass')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Apply Filter Using Xarray apply_ufunc" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "ds2_hr_lpanom_dt = xr.apply_ufunc(signal.sosfiltfilt, filtsos, ds2_hr_annanom_dt.fillna(0), kwargs={'padtype':'even','axis':0}).where(ds2_hr_annanom_dt.notnull())\n", "ds3_hr_lpanom_dt = xr.apply_ufunc(signal.sosfiltfilt, filtsos, ds3_hr_annanom_dt.fillna(0), kwargs={'padtype':'even','axis':0}).where(ds3_hr_annanom_dt.notnull())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Detrended, filtered Std Dev:\n", "ds2_hr_anndtstd = ds2_hr_annanom_dt.std('time')\n", "ds2_hr_lpdtstd = ds2_hr_lpanom_dt.std('time')\n", "ds3_hr_anndtstd = ds3_hr_annanom_dt.std('time')\n", "ds3_hr_lpdtstd = ds3_hr_lpanom_dt.std('time')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# First, create low-pass-filtered, detrended timeseries that includes the mean\n", "ds2_hr_lpann_dt = ds2_hr_lpanom_dt + ds2_hr_mean\n", "ds3_hr_lpann_dt = ds3_hr_lpanom_dt + ds3_hr_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LSW density range determined here:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([-0.15392032, 0.37033764, 1.42853378, 1.93606328, 0.55137143,\n", " 0.01353341]),\n", " array([36.925, 36.975, 37.025, 37.075, 37.125, 37.175]))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds3_hr_lpann_dt.isel(wmf_region=4).mean('time').WMF.sel(sigma_wmf=slice(36.9,37.2)).values,ds3_hr_lpann_dt.sel(sigma_wmf=slice(36.9,37.2)).sigma_wmf.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fig. 4" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Mean and Variability of WMT, WMF\n", "fig, ((ax1, ax3, ax5),(ax2,ax4,ax6)) = plt.subplots(nrows=2, ncols=3, figsize=(16, 10))\n", "fsize=14\n", "\n", "ylat1 = 55.\n", "ylat2 = 45.\n", "ylim1 = [-1,3]\n", "ylim1a = [0,1]\n", "ylim2 = [-6,9]\n", "ylim2a = [0,1.5]\n", "xlim = [36.4,37.3]\n", "legloc = 'upper left'\n", "\n", "sigma_wmf = ds3_hr_lpann_dt.sigma_wmf\n", "\n", "hr_lsw = [36.95, 37.175]\n", "hr_dlsw = [37.0625, 37.175]\n", "ax1.set_ylabel('Sv')\n", "ax1.set_ylim(ylim1)\n", "ax1.set_xlim(xlim)\n", "ax1.set_title('A) Mean WMF', fontdict={'size':fsize})\n", "labwmf = ds3_hr_lpann_dt.isel(wmf_region=4).mean('time').WMF\n", "labwmfsum = labwmf.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "irmwmf = ds3_hr_lpann_dt.isel(wmf_region=3).mean('time').WMF\n", "irmwmfsum = irmwmf.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "spgwwmf = ds3_hr_lpann_dt.isel(wmf_region=1).mean('time').WMF\n", "spgwwmfsum = spgwwmf.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "spgewmf = ds3_hr_lpann_dt.isel(wmf_region=2).mean('time').WMF\n", "spgewmfsum = spgewmf.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "labspgwmf = ds3_hr_lpann_dt.isel(wmf_region=9).mean('time').WMF\n", "labspgwmfsum = labspgwmf.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "irmspgwmf = ds3_hr_lpann_dt.isel(wmf_region=10).mean('time').WMF\n", "irmspgwmfsum = irmspgwmf.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "ginwmf = ds3_hr_lpann_dt.isel(wmf_region=5).mean('time').WMF\n", "allbutlabwmf = ds3_hr_lpann_dt.isel(wmf_region=7).mean('time').WMF\n", "allbutlabspgwmf = ds3_hr_lpann_dt.isel(wmf_region=11).mean('time').WMF\n", "ax1.plot(sigma_wmf, spgewmf,linewidth=2,marker='.',label='SPG-se',color='r')\n", "ax1.plot(sigma_wmf, irmwmf,linewidth=2,marker='.',label='IRM',color='darkorange')\n", "ax1.plot(sigma_wmf, ginwmf,linewidth=2,marker='.',label='GIN',color='g')\n", "ax1.plot(sigma_wmf, spgwwmf,linewidth=2,marker='.',label='SPG-sw',color='lightskyblue')\n", "ax1.plot(sigma_wmf, labwmf,linewidth=2,marker='.',label='LAB',color='b')\n", "ax1.plot(hr_lsw,[-0.5,-0.5],color='k',marker='|')\n", "ax1.plot(hr_dlsw,[-0.5,-0.5],color='k',marker='|')\n", "ax1.text(37.03,-0.8, 'LSW')\n", "ax1.grid()\n", "ax1.legend(loc=legloc)\n", "#ax1.set_xticklabels([])\n", "\n", "ax2.set_ylabel('Sv')\n", "ax2.set_ylim(ylim2a)\n", "ax2.set_xlim(xlim)\n", "ax2.set_title('D) WMF LF Std Dev', fontdict={'size':fsize})\n", "labwmfsd = ds3_hr_lpdtstd.isel(wmf_region=4).WMF\n", "irmwmfsd = ds3_hr_lpdtstd.isel(wmf_region=3).WMF\n", "spgwwmfsd = ds3_hr_lpdtstd.isel(wmf_region=1).WMF\n", "spgewmfsd = ds3_hr_lpdtstd.isel(wmf_region=2).WMF\n", "labspgwmfsd = ds3_hr_lpdtstd.isel(wmf_region=9).WMF\n", "irmspgwmfsd = ds3_hr_lpdtstd.isel(wmf_region=10).WMF\n", "ginwmfsd = ds3_hr_lpdtstd.isel(wmf_region=5).WMF\n", "allbutlabwmfsd = ds3_hr_lpdtstd.isel(wmf_region=7).WMF\n", "allbutlabspgwmfsd = ds3_hr_lpdtstd.isel(wmf_region=11).WMF\n", "ax2.plot(sigma_wmf, spgewmfsd,linewidth=2,marker='.',label='SPG-se',color='r')\n", "ax2.plot(sigma_wmf, irmwmfsd,linewidth=2,marker='.',label='IRM',color='darkorange')\n", "ax2.plot(sigma_wmf, ginwmfsd,linewidth=2,marker='.',label='GIN',color='g')\n", "ax2.plot(sigma_wmf, spgwwmfsd,linewidth=2,marker='.',label='SPG-sw',color='lightskyblue')\n", "ax2.plot(sigma_wmf, labwmfsd,linewidth=2,marker='.',label='LAB',color='b')\n", "ax2.plot(hr_lsw,[1.1,1.1],color='k',marker='|')\n", "ax2.plot(hr_dlsw,[1.1,1.1],color='k',marker='|')\n", "ax2.text(37.03,1.15, 'LSW')\n", "ax2.grid()\n", "ax2.legend(loc=legloc)\n", "ax2.set_xlabel(r'$\\sigma_2\\; (kg/m^{3})$')\n", "#ax2.set_xticklabels([])\n", "\n", "lr_lsw = [36.95, 37.075]\n", "#ax3.set_ylabel('Sv')\n", "ax3.set_ylim(ylim1)\n", "ax3.set_xlim(xlim)\n", "ax3.set_title('B) Mean WMF', fontdict={'size':fsize})\n", "#ax3.plot(sigma_wmf, irmspgwmf,linewidth=2,marker='.',label='IRM+SPG-east',color='r')\n", "ax3.plot(sigma_wmf, allbutlabspgwmf,linewidth=2,marker='.',label='ALL-(LAB+SPG-sw)',color='r')\n", "ax3.plot(sigma_wmf, labspgwmf,linewidth=2,marker='.',label='LAB+SPG-sw',color='b')\n", "ax3.plot(hr_lsw,[-0.5,-0.5],color='k',marker='|')\n", "ax3.plot(hr_dlsw,[-0.5,-0.5],color='k',marker='|')\n", "ax3.text(37.03,-0.8, 'LSW')\n", "ax3.grid()\n", "ax3.legend(loc=legloc)\n", "#ax3.set_xlabel(r'$\\sigma_2\\; (kg/m^{3})$')\n", "\n", "#ax4.set_ylabel('Sv')\n", "ax4.set_ylim(ylim2a)\n", "ax4.set_xlim(xlim)\n", "ax4.set_title('E) WMF LF Std Dev', fontdict={'size':fsize})\n", "#ax4.plot(sigma_wmf, irmspgwmfsd,linewidth=2,marker='.',label='IRM+SPG-east',color='r')\n", "ax4.plot(sigma_wmf, allbutlabspgwmfsd,linewidth=2,marker='.',label='ALL-(LAB+SPG-sw)',color='r')\n", "ax4.plot(sigma_wmf, labspgwmfsd,linewidth=2,marker='.',label='LAB+SPG-sw',color='b')\n", "ax4.plot(hr_lsw,[1.1,1.1],color='k',marker='|')\n", "ax4.plot(hr_dlsw,[1.1,1.1],color='k',marker='|')\n", "ax4.text(37.03,1.15, 'LSW')\n", "ax4.grid()\n", "ax4.legend(loc=legloc)\n", "ax4.set_xlabel(r'$\\sigma_2\\; (kg/m^{3})$')\n", "\n", "lr_lsw = [36.95, 37.075]\n", "#ax5.set_ylabel('Sv')\n", "ax5.set_ylim(ylim1)\n", "ax5.set_xlim(xlim)\n", "ax5.set_title('C) Mean WMF', fontdict={'size':fsize})\n", "ax5.plot(sigma_wmf, allbutlabwmf,linewidth=2,marker='.',label='ALL-LAB',color='r')\n", "ax5.plot(sigma_wmf, labwmf,linewidth=2,marker='.',label='LAB',color='b')\n", "ax5.plot(hr_lsw,[-0.5,-0.5],color='k',marker='|')\n", "ax5.plot(hr_dlsw,[-0.5,-0.5],color='k',marker='|')\n", "ax5.text(37.03,-0.8, 'LSW')\n", "ax5.grid()\n", "ax5.legend(loc=legloc)\n", "#ax5.set_xlabel(r'$\\sigma_2\\; (kg/m^{3})$')\n", "\n", "#ax6.set_ylabel('Sv')\n", "ax6.set_ylim(ylim2a)\n", "ax6.set_xlim(xlim)\n", "ax6.set_title('F) WMF LF Std Dev', fontdict={'size':fsize})\n", "ax6.plot(sigma_wmf, allbutlabwmfsd,linewidth=2,marker='.',label='ALL-LAB',color='r')\n", "ax6.plot(sigma_wmf, labwmfsd,linewidth=2,marker='.',label='LAB',color='b')\n", "ax6.plot(hr_lsw,[1.1,1.1],color='k',marker='|')\n", "ax6.plot(hr_dlsw,[1.1,1.1],color='k',marker='|')\n", "ax6.text(37.03,1.15, 'LSW')\n", "ax6.grid()\n", "ax6.legend(loc=legloc)\n", "ax6.set_xlabel(r'$\\sigma_2\\; (kg/m^{3})$')\n", "\n", "plt.savefig('Fig_4.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Table 1 values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Formation Mean" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[36.95, 37.175]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hr_lsw" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[37.0625, 37.175]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hr_dlsw = [(hr_lsw[1]-hr_lsw[0])*0.5 +hr_lsw[0], hr_lsw[1]]\n", "hr_dlsw" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "meanlsw = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "meanlsw = meanlsw.mean('time')" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MEAN LSW\n", "All (>0) : 7.85\n", "SPG_west (1) : 1.31\n", "SPG_east (2) : 0.67\n", "Irminger Sea (3) : 0.79\n", "Labrador Sea (4) : 4.30\n", "Norwegian Sea (5) : 0.96\n", "Arctic (6) : -0.19\n", "ALL minus LAB (7) : 3.55\n", "IRM+SPG (8) : 2.77\n", "LAB+SPG-west (9) : 5.61\n", "IRM+SPG-east (10) : 1.46\n", "ALL minus LAB+SPG-west (11) : 2.24\n" ] } ], "source": [ "print(\"MEAN LSW\")\n", "for i in range(meanlsw.wmf_region.size):\n", " print(\"{} : {:10.2f}\".format(meanlsw.wmf_region.values[i], meanlsw.values[i]))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7146496815286625" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5.61/7.85" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Is LSW WMF normally distributed?\n", "All (>0) : 7.8496012687683105: 0.3117499817513819: True\n", "SPG_west (1) : 1.3107932806015015: 0.00012524101747473962: False\n", "SPG_east (2) : 0.6691458821296692: 1.123785392451955e-07: False\n", "Irminger Sea (3) : 0.7938430905342102: 0.03284722731971476: False\n", "Labrador Sea (4) : 4.296127796173096: 0.4302885219131908: True\n", "Norwegian Sea (5) : 0.9653928875923157: 0.658142505516073: True\n", "Arctic (6) : -0.1857687383890152: 0.6739894935055448: True\n", "ALL minus LAB (7) : 3.5534067153930664: 0.6718147480898826: True\n", "IRM+SPG (8) : 2.773782253265381: 0.0029619411941039587: False\n", "LAB+SPG-west (9) : 5.606922149658203: 0.26294550497652924: True\n", "IRM+SPG-east (10) : 1.4629889726638794: 0.011665286238051577: False\n", "ALL minus LAB+SPG-west (11) : 2.242612600326538: 0.24279731610695787: True\n" ] } ], "source": [ "# test for normality\n", "print(\"Is LSW WMF normally distributed?\")\n", "alpha = 0.05\n", "for i in range(meanlsw.wmf_region.size):\n", " x1 = ds3_hr_ann_dt.WMF.isel(wmf_region=i).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", " mean = x1.mean('time')\n", " #D, p = stats.shapiro(x1)\n", " #D, p = stats.kstest(x1,'norm')\n", " D, p = stats.normaltest(x1)\n", " if (p>=alpha): \n", " norm=\"True\" \n", " else: \n", " norm=\"False\"\n", " print(\"{} : {}: {}: {}\".format(meanlsw.wmf_region.values[i], mean.values, p, norm))" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB vs. (ALL-LAB)\n", "p=6.5192095087460405e-12 : Regions have different mean\n" ] } ], "source": [ "# Welch's t-test to test for different means:\n", "# Null hypothesis: samples have identical means\n", "print(\"LAB vs. (ALL-LAB)\")\n", "alpha = 0.05\n", "x1 = ds3_hr_ann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf').isel(wmf_region=4)\n", "x2 = ds3_hr_ann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf').isel(wmf_region=7)\n", "D, p = stats.ttest_ind(x1,x2,equal_var=False)\n", "if (p>=alpha): \n", " result=\"Regions have the same mean\" \n", "else: \n", " result=\"Regions have different mean\"\n", "print(\"p={} : {}\".format(p, result))" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB+SPG-west vs. (ALL-LAB+SPG-west)\n", "p=6.305628605491549e-115 : Regions have different mean\n" ] } ], "source": [ "# Welch's t-test to test for different means:\n", "# Null hypothesis: samples have identical means\n", "print(\"LAB+SPG-west vs. (ALL-LAB+SPG-west)\")\n", "alpha = 0.05\n", "x1 = ds3_hr_ann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf').isel(wmf_region=9)\n", "x2 = ds3_hr_ann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf').isel(wmf_region=11)\n", "D, p = stats.ttest_ind(x1,x2,equal_var=False)\n", "if (p>=alpha): \n", " result=\"Regions have the same mean\" \n", "else: \n", " result=\"Regions have different mean\"\n", "print(\"p={} : {}\".format(p, result))" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "hr_dlsw = [(hr_lsw[1]-hr_lsw[0])*0.5 +hr_lsw[0], hr_lsw[1]]\n", "meandlsw = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "meandlsw = meandlsw.mean('time')" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MEAN dLSW\n", "All (>0) : 5.82\n", "SPG_west (1) : 0.92\n", "SPG_east (2) : 0.71\n", "Irminger Sea (3) : 1.16\n", "Labrador Sea (4) : 2.50\n", "Norwegian Sea (5) : 0.56\n", "Arctic (6) : -0.03\n", "ALL minus LAB (7) : 3.31\n", "IRM+SPG (8) : 2.78\n", "LAB+SPG-west (9) : 3.42\n", "IRM+SPG-east (10) : 1.87\n", "ALL minus LAB+SPG-west (11) : 2.40\n" ] } ], "source": [ "print(\"MEAN dLSW\")\n", "for i in range(meandlsw.wmf_region.size):\n", " print(\"{} : {:10.2f}\".format(meandlsw.wmf_region.values[i], meandlsw.values[i]))" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5876288659793814" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3.42/5.82" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dLSW WMF normally distributed?\n", "All (>0) : 5.817356109619141: 1.0938979357888456e-06: False\n", "SPG_west (1) : 0.9161280393600464: 6.107232775054428e-11: False\n", "SPG_east (2) : 0.7087029218673706: 1.826840978838562e-12: False\n", "Irminger Sea (3) : 1.1575684547424316: 1.2270733122932143e-06: False\n", "Labrador Sea (4) : 2.500304937362671: 2.0760842289746506e-07: False\n", "Norwegian Sea (5) : 0.5648283362388611: 0.9572058320045471: True\n", "Arctic (6) : -0.030182521790266037: 0.7389059662818909: True\n", "ALL minus LAB (7) : 3.3170454502105713: 5.938991307630204e-07: False\n", "IRM+SPG (8) : 2.7823994159698486: 8.314824384569874e-09: False\n", "LAB+SPG-west (9) : 3.4164340496063232: 4.015182142325102e-08: False\n", "IRM+SPG-east (10) : 1.8662716150283813: 5.137874481420113e-08: False\n", "ALL minus LAB+SPG-west (11) : 2.400916814804077: 5.152928861207329e-05: False\n" ] } ], "source": [ "# test for normality\n", "print(\"dLSW WMF normally distributed?\")\n", "alpha = 0.05\n", "for i in range(meandlsw.wmf_region.size):\n", " x1 = ds3_hr_ann_dt.WMF.isel(wmf_region=i).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", " mean = x1.mean('time')\n", " D, p = stats.shapiro(x1)\n", " #D, p = stats.normaltest(x1)\n", " if (p>=alpha): \n", " norm=\"True\" \n", " else: \n", " norm=\"False\"\n", " print(\"{} : {}: {}: {}\".format(meandlsw.wmf_region.values[i], mean.values, p, norm))" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dLSW WMF normally distributed?\n" ] }, { "data": { "text/plain": [ "(0.18005413633613898,\n", " array([0.569, 0.648, 0.777, 0.906, 1.078]),\n", " array([15. , 10. , 5. , 2.5, 1. ]))" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# test for normality\n", "print(\"dLSW WMF normally distributed?\")\n", "alpha = 0.05\n", "i=5\n", "x1 = ds3_hr_ann_dt.WMF.isel(wmf_region=i).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "mean = x1.mean('time')\n", "stat,crit,sign= stats.anderson(x1.values)\n", "#results.statistic,results.critical_values,results.significance_level\n", "stat, crit, sign" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB vs. (ALL-LAB)\n", "p=3.8667280200449665e-17 : Regions have different mean\n" ] } ], "source": [ "# Welch's t-test to test for different means:\n", "# Null hypothesis: samples have identical means\n", "print(\"LAB vs. (ALL-LAB)\")\n", "alpha = 0.05\n", "x1 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf').isel(wmf_region=4)\n", "x2 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf').isel(wmf_region=7)\n", "D, p = stats.ttest_ind(x1,x2,equal_var=False)\n", "if (p>=alpha): \n", " result=\"Regions have the same mean\" \n", "else: \n", " result=\"Regions have different mean\"\n", "print(\"p={} : {}\".format(p, result))" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB+SPG-west vs. (ALL-LAB+SPG-west)\n", "p=2.811494450206955e-23 : Regions have different mean\n" ] } ], "source": [ "# Welch's t-test to test for different means:\n", "# Null hypothesis: samples have identical means\n", "print(\"LAB+SPG-west vs. (ALL-LAB+SPG-west)\")\n", "alpha = 0.05\n", "x1 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf').isel(wmf_region=9)\n", "x2 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf').isel(wmf_region=11)\n", "D, p = stats.ttest_ind(x1,x2,equal_var=False)\n", "if (p>=alpha): \n", " result=\"Regions have the same mean\" \n", "else: \n", " result=\"Regions have different mean\"\n", "print(\"p={} : {}\".format(p, result))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Formation LF Variability" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "sdlsw = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "sdlsw = sdlsw.std('time')" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LF STD DEV LSW\n", "All (>0) : 0.99232\n", "SPG_west (1) : 0.26569\n", "SPG_east (2) : 0.16832\n", "Irminger Sea (3) : 0.16822\n", "Labrador Sea (4) : 0.55947\n", "Norwegian Sea (5) : 0.26363\n", "Arctic (6) : 0.17752\n", "ALL minus LAB (7) : 0.52666\n", "IRM+SPG (8) : 0.52371\n", "LAB+SPG-west (9) : 0.78230\n", "IRM+SPG-east (10) : 0.30468\n", "ALL minus LAB+SPG-west (11) : 0.36320\n" ] } ], "source": [ "print(\"LF STD DEV LSW\")\n", "for i in range(sdlsw.wmf_region.size):\n", " print(\"{} : {:13.5f}\".format(sdlsw.wmf_region.values[i], sdlsw.values[i]))" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LSW: LAB vs. (ALL-LAB)\n", "p=0.19368028525262637 : Regions have the same variance\n" ] } ], "source": [ "# Levene's to test for differences in variance:\n", "# Null hypothesis: samples have identical variance\n", "print(\"LSW: LAB vs. (ALL-LAB)\")\n", "alpha = 0.05\n", "x1 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf').isel(wmf_region=4)\n", "x2 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf').isel(wmf_region=7)\n", "D, p = stats.levene(x1,x2)\n", "if (p>=alpha): \n", " result=\"Regions have the same variance\" \n", "else: \n", " result=\"Regions have different variance\"\n", "print(\"p={} : {}\".format(p, result))" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LSW: LAB+SPG-west vs. (ALL-LAB+SPG-west)\n", "p=3.377569035602758e-33 : Regions have different variance\n" ] } ], "source": [ "# Levene's to test for differences in variance:\n", "# Null hypothesis: samples have identical variance\n", "print(\"LSW: LAB+SPG-west vs. (ALL-LAB+SPG-west)\")\n", "alpha = 0.05\n", "x1 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf').isel(wmf_region=9)\n", "x2 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf').isel(wmf_region=11)\n", "D, p = stats.levene(x1,x2)\n", "if (p>=alpha): \n", " result=\"Regions have the same variance\" \n", "else: \n", " result=\"Regions have different variance\"\n", "print(\"p={} : {}\".format(p, result))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "sddlsw = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "sddlsw = sddlsw.std('time')" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "STD DEV dLSW\n", "All (>0) : 2.23\n", "SPG_west (1) : 0.43\n", "SPG_east (2) : 0.37\n", "Irminger Sea (3) : 0.48\n", "Labrador Sea (4) : 1.05\n", "Norwegian Sea (5) : 0.21\n", "Arctic (6) : 0.18\n", "ALL minus LAB (7) : 1.24\n", "IRM+SPG (8) : 1.21\n", "LAB+SPG-west (9) : 1.46\n", "IRM+SPG-east (10) : 0.82\n", "ALL minus LAB+SPG-west (11) : 0.85\n" ] } ], "source": [ "print(\"STD DEV dLSW\")\n", "for i in range(sddlsw.wmf_region.size):\n", " x1 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", " D, p = stats.shapiro(x1)\n", " print(\"{} : {:10.2f}\".format(sddlsw.wmf_region.values[i], sddlsw.values[i]))" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dLSW: LAB vs. (ALL-LAB)\n", "p=0.003372836282617322 : Regions have different variance\n" ] } ], "source": [ "# Levene's to test for differences in variance:\n", "# Null hypothesis: samples have identical variance\n", "print(\"dLSW: LAB vs. (ALL-LAB)\")\n", "alpha = 0.05\n", "x1 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf').isel(wmf_region=4)\n", "x2 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf').isel(wmf_region=7)\n", "D, p = stats.levene(x1,x2)\n", "if (p>=alpha): \n", " result=\"Regions have the same variance\" \n", "else: \n", " result=\"Regions have different variance\"\n", "print(\"p={} : {}\".format(p, result))" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dLSW: LAB+SPG-west vs. (ALL-LAB+SPG-west)\n", "p=1.6512393450946943e-19 : Regions have different variance\n" ] } ], "source": [ "# Levene's to test for differences in variance:\n", "# Null hypothesis: samples have identical variance\n", "print(\"dLSW: LAB+SPG-west vs. (ALL-LAB+SPG-west)\")\n", "alpha = 0.05\n", "x1 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf').isel(wmf_region=9)\n", "x2 = ds3_hr_lpann_dt.WMF.sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf').isel(wmf_region=11)\n", "D, p = stats.levene(x1,x2)\n", "if (p>=alpha): \n", " result=\"Regions have the same variance\" \n", "else: \n", " result=\"Regions have different variance\"\n", "print(\"p={} : {}\".format(p, result))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlations" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "def lag_linregress_3D(x, y, lagx=0, lagy=0):\n", " \"\"\"\n", " Input: Two xr.Datarrays of any dimensions with the first dim being time. \n", " Thus the input data could be a 1D time series, or for example, have three \n", " dimensions (time,lat,lon). \n", " Datasets can be provided in any order, but note that the regression slope \n", " and intercept will be calculated for y with respect to x.\n", " Output: Covariance, correlation, regression slope and intercept, p-value, \n", " and standard error on regression between the two datasets along their \n", " aligned time dimension. \n", " Lag values can be assigned to either of the data, with lagx shifting x, and\n", " lagy shifting y, with the specified lag amount. \n", " \"\"\" \n", " #1. Ensure that the data are properly alinged to each other. \n", " x,y = xr.align(x,y)\n", "\n", " #2. Add lag information if any, and shift the data accordingly\n", " if lagx!=0:\n", "\n", " # If x lags y by 1, x must be shifted 1 step backward. \n", " # But as the 'zero-th' value is nonexistant, xr assigns it as invalid \n", " # (nan). Hence it needs to be dropped\n", " #x = x.shift(time = -lagx).dropna(dim='time')\n", " if lagx>0: x = x.shift(time = -lagx).isel(time=slice(0,-lagx))\n", " if lagx<0: x = x.shift(time = -lagx).isel(time=slice(-lagx,None))\n", "\n", " # Next important step is to re-align the two datasets so that y adjusts\n", " # to the changed coordinates of x\n", " x,y = xr.align(x,y)\n", "\n", " if lagy!=0:\n", " #y = y.shift(time = -lagy).dropna(dim='time')\n", " if lagy>0: y = y.shift(time = -lagy).isel(time=slice(0,-lagy))\n", " if lagy<0: y = y.shift(time = -lagy).isel(time=slice(-lagy,None))\n", " x,y = xr.align(x,y)\n", "\n", " #3. Compute data length, mean and standard deviation along time axis: \n", " n = y.notnull().sum(dim='time')\n", " xmean = x.mean(axis=0)\n", " ymean = y.mean(axis=0)\n", " xstd = x.std(axis=0)\n", " ystd = y.std(axis=0)\n", "\n", " #4. Compute covariance along time axis\n", " #cov = np.sum((x - xmean)*(y - ymean), axis=0)/(n)\n", " cov = ((x - xmean) * (y - ymean)).mean(axis=0)\n", "\n", " #5. Compute correlation along time axis\n", " cor = cov/(xstd*ystd)\n", "\n", " #6. Compute regression slope and intercept:\n", " slope = cov/(xstd**2)\n", " intercept = ymean - xmean*slope \n", " \n", " #6.5. Compute effective degrees of freedom taking into account autocorrelation\n", " xp1 = x.shift(time=-1).isel(time=slice(0,-1))\n", " xtmp,xp1 = xr.align(x,xp1)\n", " autocorrx = xr.corr(xtmp,xp1,dim='time')\n", " yp1 = y.shift(time=-1).isel(time=slice(0,-1))\n", " ytmp,yp1 = xr.align(y,yp1)\n", " autocorry = xr.corr(ytmp,yp1,dim='time')\n", " neff = n*(1-autocorrx*autocorry)/(1+autocorrx*autocorry)\n", "\n", " #7. Compute P-value and standard error\n", " #Compute t-statistics\n", " #tstats = cor*np.sqrt(n-2)/np.sqrt(1-cor**2)\n", " tstats = cor*np.sqrt(neff-2)/np.sqrt(1-cor**2)\n", " stderr = slope/tstats\n", "\n", " from scipy.stats import t\n", " #pval = t.sf(tstats, neff-2)*2\n", " pval = 2.*(1-t.cdf(np.abs(slope/stderr),neff-2))\n", " pval = xr.DataArray(pval, dims=cor.dims, coords=cor.coords)\n", " \n", " #8. Combine all stats into single output xarray\n", " stat = xr.DataArray(['covariance','correlation','slope','intercept','pval','stderr'],dims=\"stat\",name=\"stat\")\n", " xrout = xr.concat([cov,cor,slope,intercept,pval,stderr],dim=stat)\n", "\n", " return xrout" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "def concat_correlations(x, y, lagyrange):\n", " corrlist = []\n", " lag = xr.DataArray([i for i in lagyrange],dims='lag',name='lag')\n", " for i in lagyrange:\n", " z = lag_linregress_3D(x,y,lagy=i)\n", "# z.name = y.name + '_corr_'+x.name\n", " corrlist.append(z)\n", " return xr.concat(corrlist,dim=lag)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# fig S4" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'wmf_region' (wmf_region: 12)>\n",
       "array(['All (>0)', 'SPG_west (1)', 'SPG_east (2)', 'Irminger Sea (3)',\n",
       "       'Labrador Sea (4)', 'Norwegian Sea (5)', 'Arctic (6)',\n",
       "       'ALL minus LAB (7)', 'IRM+SPG (8)', 'LAB+SPG-west (9)',\n",
       "       'IRM+SPG-east (10)', 'ALL minus LAB+SPG-west (11)'], dtype=object)\n",
       "Coordinates:\n",
       "  * wmf_region  (wmf_region) object 'All (>0)' ... 'ALL minus LAB+SPG-west (11)'
" ], "text/plain": [ "\n", "array(['All (>0)', 'SPG_west (1)', 'SPG_east (2)', 'Irminger Sea (3)',\n", " 'Labrador Sea (4)', 'Norwegian Sea (5)', 'Arctic (6)',\n", " 'ALL minus LAB (7)', 'IRM+SPG (8)', 'LAB+SPG-west (9)',\n", " 'IRM+SPG-east (10)', 'ALL minus LAB+SPG-west (11)'], dtype=object)\n", "Coordinates:\n", " * wmf_region (wmf_region) object 'All (>0)' ... 'ALL minus LAB+SPG-west (11)'" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds3_hr_lpann_dt.wmf_region" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lagrange = range(-10,11,1)\n", "moc45 = ds2_hr_lpann_dt.MOCsig.isel(transport_reg=1).isel(moc_comp=0).sel(lat_aux_grid=45.,method='nearest').max('moc_s')\n", "lswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "dlswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "llswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "xcorr1 = concat_correlations(lswwmf,dlswwmf, lagrange)\n", "xcorr2 = concat_correlations(lswwmf,llswwmf, lagrange)\n", "xcorr3 = concat_correlations(llswwmf,dlswwmf, lagrange)\n", "xcorr4 = concat_correlations(moc45,lswwmf, lagrange)\n", "xcorr5 = concat_correlations(moc45,llswwmf, lagrange)\n", "xcorr6 = concat_correlations(moc45,dlswwmf, lagrange)\n", "\n", "# Year 200-500 hovmuller plots\n", "fig = plt.figure(figsize=(12, 12))\n", "spec = fig.add_gridspec(ncols=2, nrows=2, figure=fig)\n", "ax1 = fig.add_subplot(spec[0, 0])\n", "ax2 = fig.add_subplot(spec[0, 1])\n", "ax3 = fig.add_subplot(spec[1, 0])\n", "ax4 = fig.add_subplot(spec[1, 1])\n", "\n", "xlim=[-10,10]\n", "ylim=[-1.,1]\n", "yticks = [-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1]\n", "xticks = np.arange(-10,10,2)\n", "fsize=12\n", "lw = 0.5\n", "siglvl = 0.05\n", "\n", "#ax1.set_xlabel('Lag (years)')\n", "ax1.set_ylabel('correlation')\n", "ax1.set_ylim(ylim)\n", "ax1.set_xlim(xlim)\n", "ax1.set_title(r'A) Cross-correlations of LSW (LAB)', fontdict={'size':fsize}, loc='left')\n", "plt1a = ax1.plot(xcorr1.lag, xcorr1.sel(stat='correlation'),color='k',linewidth=1)\n", "work1b = xcorr1.where(xcorr1.sel(stat='pval') < siglvl)\n", "plt1b = ax1.plot(work1b.lag,work1b.sel(stat='correlation'), color='k', linewidth=3,label=r'r(LSW, dLSW)')\n", "plt1c = ax1.plot(xcorr2.lag, xcorr2.sel(stat='correlation'),color='b',linewidth=1)\n", "work1d = xcorr2.where(xcorr2.sel(stat='pval') < siglvl)\n", "plt1d = ax1.plot(work1d.lag,work1d.sel(stat='correlation'), color='b', linewidth=3,label=r'r(LSW, lLSW)')\n", "plt1e = ax1.plot(xcorr3.lag, xcorr3.sel(stat='correlation'),color='r',linewidth=1)\n", "work1f = xcorr3.where(xcorr3.sel(stat='pval') < siglvl)\n", "plt1f = ax1.plot(work1f.lag,work1f.sel(stat='correlation'), color='r', linewidth=3,label=r'r(lLSW, dLSW)')\n", "ax1.grid()\n", "ax1.set_yticks(yticks)\n", "ax1.set_xticks(xticks)\n", "ax1.legend(loc='lower right')\n", "\n", "#ax2.set_xlabel('Lag (years)')\n", "#ax2.set_ylabel('correlation')\n", "ax2.set_ylim(ylim)\n", "ax2.set_xlim(xlim)\n", "tmpstr = r'$\\Psi_{max} \\; at \\; 45^{\\circ}N$'\n", "ax2.set_title(r'B) LSW (LAB) vs. '+tmpstr, fontdict={'size':fsize}, loc='left')\n", "plt2a = ax2.plot(xcorr4.lag, xcorr4.sel(stat='correlation'),color='k',linewidth=1)\n", "work2b = xcorr4.where(xcorr4.sel(stat='pval') < siglvl)\n", "lab2b = r'r($\\Psi_{max}$, LSW)'\n", "plt2b = ax2.plot(work2b.lag,work2b.sel(stat='correlation'), color='k', linewidth=3,label=lab2b)\n", "plt2c = ax2.plot(xcorr5.lag, xcorr5.sel(stat='correlation'),color='b',linewidth=1)\n", "work2d = xcorr5.where(xcorr5.sel(stat='pval') < siglvl)\n", "lab2c = r'r($\\Psi_{max}$, lLSW)'\n", "plt2d = ax2.plot(work2d.lag,work2d.sel(stat='correlation'), color='b', linewidth=3,label=lab2c)\n", "plt2e = ax2.plot(xcorr6.lag, xcorr6.sel(stat='correlation'),color='r',linewidth=1)\n", "work2f = xcorr6.where(xcorr6.sel(stat='pval') < siglvl)\n", "lab2d = r'r($\\Psi_{max}$, dLSW)'\n", "plt2f = ax2.plot(work2f.lag,work2f.sel(stat='correlation'), color='r', linewidth=3,label=lab2d)\n", "ax2.grid()\n", "ax2.set_yticks(yticks)\n", "ax2.set_xticks(xticks)\n", "ax2.legend(loc='lower right')\n", "\n", "#lswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "#dlswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "#llswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "lswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=3).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "dlswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=3).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "llswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=3).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "xcorr1 = concat_correlations(lswwmf,lswwmf2, lagrange)\n", "xcorr2 = concat_correlations(dlswwmf,dlswwmf2, lagrange)\n", "xcorr3 = concat_correlations(llswwmf,llswwmf2, lagrange)\n", "#lswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=10).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "#dlswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=10).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "#llswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=10).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "xcorr4 = concat_correlations(moc45,lswwmf2, lagrange)\n", "xcorr5 = concat_correlations(moc45,llswwmf2, lagrange)\n", "xcorr6 = concat_correlations(moc45,dlswwmf2, lagrange)\n", "\n", "ax3.set_ylabel('correlation')\n", "ax3.set_xlabel('Lag (years)')\n", "ax3.set_ylim(ylim)\n", "ax3.set_xlim(xlim)\n", "ax3.set_title(r'C) LSW (LAB) vs. LSW (IRM)', fontdict={'size':fsize}, loc='left')\n", "plt3a = ax3.plot(xcorr1.lag, xcorr1.sel(stat='correlation'),color='k',linewidth=1)\n", "work3b = xcorr1.where(xcorr1.sel(stat='pval') < siglvl)\n", "plt3b = ax3.plot(work3b.lag,work3b.sel(stat='correlation'), color='k', linewidth=3,label=r'r(LSWlab, LSWirm)')\n", "plt3c = ax3.plot(xcorr2.lag, xcorr2.sel(stat='correlation'),color='b',linewidth=1)\n", "work3d = xcorr2.where(xcorr2.sel(stat='pval') < siglvl)\n", "plt3d = ax3.plot(work3d.lag,work3d.sel(stat='correlation'), color='b', linewidth=3,label=r'r(dLSWlab, dLSWirm)')\n", "plt3e = ax3.plot(xcorr3.lag, xcorr3.sel(stat='correlation'),color='r',linewidth=1)\n", "work3e = xcorr3.where(xcorr3.sel(stat='pval') < siglvl)\n", "plt3f = ax3.plot(work3e.lag,work3e.sel(stat='correlation'), color='r', linewidth=3,label=r'r(lLSWlab, lLSWirm)')\n", "ax3.grid()\n", "ax3.set_yticks(yticks)\n", "ax3.set_xticks(xticks)\n", "ax3.legend(loc='lower right')\n", "\n", "ax4.set_xlabel('Lag (years)')\n", "#ax4.set_ylabel('correlation')\n", "ax4.set_ylim(ylim)\n", "ax4.set_xlim(xlim)\n", "tmpstr = r'$\\Psi_{max} \\; at \\; 45^{\\circ}N$'\n", "ax4.set_title(r'D) LSW (IRM) vs. '+tmpstr, fontdict={'size':fsize}, loc='left')\n", "plt4a = ax4.plot(xcorr4.lag, xcorr4.sel(stat='correlation'),color='k',linewidth=1)\n", "work4b = xcorr4.where(xcorr4.sel(stat='pval') < siglvl)\n", "lab4b = r'r($\\Psi_{max}$, LSW)'\n", "plt4b = ax4.plot(work4b.lag,work4b.sel(stat='correlation'), color='k', linewidth=3,label=lab4b)\n", "plt4c = ax4.plot(xcorr5.lag, xcorr5.sel(stat='correlation'),color='b',linewidth=1)\n", "work4d = xcorr5.where(xcorr5.sel(stat='pval') < siglvl)\n", "lab4c = r'r($\\Psi_{max}$, lLSW)'\n", "plt4d = ax4.plot(work4d.lag,work4d.sel(stat='correlation'), color='b', linewidth=3,label=lab4c)\n", "plt4e = ax4.plot(xcorr6.lag, xcorr6.sel(stat='correlation'),color='r',linewidth=1)\n", "work4f = xcorr6.where(xcorr6.sel(stat='pval') < siglvl)\n", "lab4d = r'r($\\Psi_{max}$, dLSW)'\n", "plt4f = ax4.plot(work4f.lag,work4f.sel(stat='correlation'), color='r', linewidth=3,label=lab4d)\n", "ax4.grid()\n", "ax4.set_yticks(yticks)\n", "ax4.set_xticks(xticks)\n", "ax4.legend(loc='lower right')\n", "\n", "#plt.savefig('fig_S5.pdf')" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'wmf_region' (wmf_region: 12)>\n",
       "array(['All (>0)', 'SPG_west (1)', 'SPG_east (2)', 'Irminger Sea (3)',\n",
       "       'Labrador Sea (4)', 'Norwegian Sea (5)', 'Arctic (6)',\n",
       "       'ALL minus LAB (7)', 'IRM+SPG (8)', 'LAB+SPG-west (9)',\n",
       "       'IRM+SPG-east (10)', 'ALL minus LAB+SPG-west (11)'], dtype=object)\n",
       "Coordinates:\n",
       "  * wmf_region  (wmf_region) object 'All (>0)' ... 'ALL minus LAB+SPG-west (11)'
" ], "text/plain": [ "\n", "array(['All (>0)', 'SPG_west (1)', 'SPG_east (2)', 'Irminger Sea (3)',\n", " 'Labrador Sea (4)', 'Norwegian Sea (5)', 'Arctic (6)',\n", " 'ALL minus LAB (7)', 'IRM+SPG (8)', 'LAB+SPG-west (9)',\n", " 'IRM+SPG-east (10)', 'ALL minus LAB+SPG-west (11)'], dtype=object)\n", "Coordinates:\n", " * wmf_region (wmf_region) object 'All (>0)' ... 'ALL minus LAB+SPG-west (11)'" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds3_hr_lpann_dt.wmf_region" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lagrange = range(-10,11,1)\n", "moc45 = ds2_hr_lpann_dt.MOCsig.isel(transport_reg=1).isel(moc_comp=0).sel(lat_aux_grid=45.,method='nearest').max('moc_s')\n", "lswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "dlswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "llswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "xcorr1 = concat_correlations(lswwmf,dlswwmf, lagrange)\n", "xcorr2 = concat_correlations(lswwmf,llswwmf, lagrange)\n", "xcorr3 = concat_correlations(llswwmf,dlswwmf, lagrange)\n", "xcorr4 = concat_correlations(moc45,lswwmf, lagrange)\n", "xcorr5 = concat_correlations(moc45,llswwmf, lagrange)\n", "xcorr6 = concat_correlations(moc45,dlswwmf, lagrange)\n", "\n", "# Year 200-500 hovmuller plots\n", "fig = plt.figure(figsize=(18, 12))\n", "spec = fig.add_gridspec(ncols=3, nrows=2, figure=fig)\n", "ax1 = fig.add_subplot(spec[0, 0])\n", "ax2 = fig.add_subplot(spec[0, 1])\n", "ax3 = fig.add_subplot(spec[0, 2])\n", "ax4 = fig.add_subplot(spec[1, 0])\n", "ax5 = fig.add_subplot(spec[1, 1])\n", "ax6 = fig.add_subplot(spec[1, 2])\n", "\n", "xlim=[-10,10]\n", "ylim=[-1.,1]\n", "yticks = [-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1]\n", "xticks = np.arange(-10,10,2)\n", "fsize=12\n", "lw = 0.5\n", "siglvl = 0.05\n", "\n", "#ax1.set_xlabel('Lag (years)')\n", "ax1.set_ylabel('correlation')\n", "ax1.set_ylim(ylim)\n", "ax1.set_xlim(xlim)\n", "ax1.set_title(r'A) Cross-correlations of LSW (LAB)', fontdict={'size':fsize}, loc='left')\n", "plt1a = ax1.plot(xcorr1.lag, xcorr1.sel(stat='correlation'),color='k',linewidth=1)\n", "work1b = xcorr1.where(xcorr1.sel(stat='pval') < siglvl)\n", "plt1b = ax1.plot(work1b.lag,work1b.sel(stat='correlation'), color='k', linewidth=3,label=r'r(LSW, dLSW)')\n", "plt1c = ax1.plot(xcorr2.lag, xcorr2.sel(stat='correlation'),color='b',linewidth=1)\n", "work1d = xcorr2.where(xcorr2.sel(stat='pval') < siglvl)\n", "plt1d = ax1.plot(work1d.lag,work1d.sel(stat='correlation'), color='b', linewidth=3,label=r'r(LSW, lLSW)')\n", "plt1e = ax1.plot(xcorr3.lag, xcorr3.sel(stat='correlation'),color='r',linewidth=1)\n", "work1f = xcorr3.where(xcorr3.sel(stat='pval') < siglvl)\n", "plt1f = ax1.plot(work1f.lag,work1f.sel(stat='correlation'), color='r', linewidth=3,label=r'r(lLSW, dLSW)')\n", "ax1.grid()\n", "ax1.set_yticks(yticks)\n", "ax1.set_xticks(xticks)\n", "ax1.legend(loc='lower right')\n", "\n", "#ax2.set_xlabel('Lag (years)')\n", "#ax2.set_ylabel('correlation')\n", "ax2.set_ylim(ylim)\n", "ax2.set_xlim(xlim)\n", "tmpstr = r'$\\Psi_{max} \\; at \\; 45^{\\circ}N$'\n", "ax2.set_title(r'B) LSW (LAB) vs. '+tmpstr, fontdict={'size':fsize}, loc='left')\n", "plt2a = ax2.plot(xcorr4.lag, xcorr4.sel(stat='correlation'),color='k',linewidth=1)\n", "work2b = xcorr4.where(xcorr4.sel(stat='pval') < siglvl)\n", "lab2b = r'r($\\Psi_{max}$, LSW)'\n", "plt2b = ax2.plot(work2b.lag,work2b.sel(stat='correlation'), color='k', linewidth=3,label=lab2b)\n", "plt2c = ax2.plot(xcorr5.lag, xcorr5.sel(stat='correlation'),color='b',linewidth=1)\n", "work2d = xcorr5.where(xcorr5.sel(stat='pval') < siglvl)\n", "lab2c = r'r($\\Psi_{max}$, lLSW)'\n", "plt2d = ax2.plot(work2d.lag,work2d.sel(stat='correlation'), color='b', linewidth=3,label=lab2c)\n", "plt2e = ax2.plot(xcorr6.lag, xcorr6.sel(stat='correlation'),color='r',linewidth=1)\n", "work2f = xcorr6.where(xcorr6.sel(stat='pval') < siglvl)\n", "lab2d = r'r($\\Psi_{max}$, dLSW)'\n", "plt2f = ax2.plot(work2f.lag,work2f.sel(stat='correlation'), color='r', linewidth=3,label=lab2d)\n", "ax2.grid()\n", "ax2.set_yticks(yticks)\n", "ax2.set_xticks(xticks)\n", "ax2.legend(loc='lower right')\n", "\n", "lswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=9).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "dlswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=9).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "llswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=9).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "xcorr4 = concat_correlations(moc45,lswwmf, lagrange)\n", "xcorr5 = concat_correlations(moc45,llswwmf, lagrange)\n", "xcorr6 = concat_correlations(moc45,dlswwmf, lagrange)\n", "ax3.set_ylim(ylim)\n", "ax3.set_xlim(xlim)\n", "tmpstr = r'$\\Psi_{max} \\; at \\; 45^{\\circ}N$'\n", "ax3.set_title(r'C) LSW (LAB+SPG-west) vs. '+tmpstr, fontdict={'size':fsize}, loc='left')\n", "plt3a = ax3.plot(xcorr4.lag, xcorr4.sel(stat='correlation'),color='k',linewidth=1)\n", "work3b = xcorr4.where(xcorr4.sel(stat='pval') < siglvl)\n", "lab3b = r'r($\\Psi_{max}$, LSW)'\n", "plt3b = ax3.plot(work3b.lag,work3b.sel(stat='correlation'), color='k', linewidth=3,label=lab3b)\n", "plt3c = ax3.plot(xcorr5.lag, xcorr5.sel(stat='correlation'),color='b',linewidth=1)\n", "work3d = xcorr5.where(xcorr5.sel(stat='pval') < siglvl)\n", "lab3c = r'r($\\Psi_{max}$, lLSW)'\n", "plt3d = ax3.plot(work3d.lag,work3d.sel(stat='correlation'), color='b', linewidth=3,label=lab3c)\n", "plt3e = ax3.plot(xcorr6.lag, xcorr6.sel(stat='correlation'),color='r',linewidth=1)\n", "work3f = xcorr6.where(xcorr6.sel(stat='pval') < siglvl)\n", "lab3d = r'r($\\Psi_{max}$, dLSW)'\n", "plt3f = ax3.plot(work3f.lag,work3f.sel(stat='correlation'), color='r', linewidth=3,label=lab3d)\n", "ax3.grid()\n", "ax3.set_yticks(yticks)\n", "ax3.set_xticks(xticks)\n", "ax3.legend(loc='lower right')\n", "\n", "\n", "#lswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "#dlswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "#llswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=4).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "lswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=3).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "dlswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=3).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "llswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=3).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "xcorr1 = concat_correlations(lswwmf,lswwmf2, lagrange)\n", "xcorr2 = concat_correlations(dlswwmf,dlswwmf2, lagrange)\n", "xcorr3 = concat_correlations(llswwmf,llswwmf2, lagrange)\n", "#lswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=10).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "#dlswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=10).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "#llswwmf2 = ds3_hr_lpann_dt.WMF.isel(wmf_region=10).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "xcorr4 = concat_correlations(moc45,lswwmf2, lagrange)\n", "xcorr5 = concat_correlations(moc45,llswwmf2, lagrange)\n", "xcorr6 = concat_correlations(moc45,dlswwmf2, lagrange)\n", "\n", "ax4.set_ylabel('correlation')\n", "ax4.set_xlabel('Lag (years)')\n", "ax4.set_ylim(ylim)\n", "ax4.set_xlim(xlim)\n", "ax4.set_title(r'D) LSW (LAB) vs. LSW (IRM)', fontdict={'size':fsize}, loc='left')\n", "plt4a = ax4.plot(xcorr1.lag, xcorr1.sel(stat='correlation'),color='k',linewidth=1)\n", "work4b = xcorr1.where(xcorr1.sel(stat='pval') < siglvl)\n", "plt4b = ax4.plot(work4b.lag,work4b.sel(stat='correlation'), color='k', linewidth=3,label=r'r(LSWlab, LSWirm)')\n", "plt4c = ax4.plot(xcorr2.lag, xcorr2.sel(stat='correlation'),color='b',linewidth=1)\n", "work4d = xcorr2.where(xcorr2.sel(stat='pval') < siglvl)\n", "plt4d = ax4.plot(work4d.lag,work4d.sel(stat='correlation'), color='b', linewidth=3,label=r'r(dLSWlab, dLSWirm)')\n", "plt4e = ax4.plot(xcorr3.lag, xcorr3.sel(stat='correlation'),color='r',linewidth=1)\n", "work4e = xcorr3.where(xcorr3.sel(stat='pval') < siglvl)\n", "plt4f = ax4.plot(work4e.lag,work4e.sel(stat='correlation'), color='r', linewidth=3,label=r'r(lLSWlab, lLSWirm)')\n", "ax4.grid()\n", "ax4.set_yticks(yticks)\n", "ax4.set_xticks(xticks)\n", "ax4.legend(loc='lower right')\n", "\n", "ax5.set_xlabel('Lag (years)')\n", "ax5.set_ylim(ylim)\n", "ax5.set_xlim(xlim)\n", "tmpstr = r'$\\Psi_{max} \\; at \\; 45^{\\circ}N$'\n", "ax5.set_title(r'E) LSW (IRM) vs. '+tmpstr, fontdict={'size':fsize}, loc='left')\n", "plt5a = ax5.plot(xcorr4.lag, xcorr4.sel(stat='correlation'),color='k',linewidth=1)\n", "work5b = xcorr4.where(xcorr4.sel(stat='pval') < siglvl)\n", "lab5b = r'r($\\Psi_{max}$, LSW)'\n", "plt5b = ax5.plot(work5b.lag,work5b.sel(stat='correlation'), color='k', linewidth=3,label=lab4b)\n", "plt5c = ax5.plot(xcorr5.lag, xcorr5.sel(stat='correlation'),color='b',linewidth=1)\n", "work5d = xcorr5.where(xcorr5.sel(stat='pval') < siglvl)\n", "lab5c = r'r($\\Psi_{max}$, lLSW)'\n", "plt5d = ax5.plot(work5d.lag,work5d.sel(stat='correlation'), color='b', linewidth=3,label=lab4c)\n", "plt5e = ax5.plot(xcorr6.lag, xcorr6.sel(stat='correlation'),color='r',linewidth=1)\n", "work5f = xcorr6.where(xcorr6.sel(stat='pval') < siglvl)\n", "lab5d = r'r($\\Psi_{max}$, dLSW)'\n", "plt5f = ax5.plot(work5f.lag,work5f.sel(stat='correlation'), color='r', linewidth=3,label=lab4d)\n", "ax5.grid()\n", "ax5.set_yticks(yticks)\n", "ax5.set_xticks(xticks)\n", "ax5.legend(loc='lower right')\n", "\n", "lswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=10).sel(sigma_wmf=slice(hr_lsw[0],hr_lsw[1])).sum('sigma_wmf')\n", "dlswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=10).sel(sigma_wmf=slice(hr_dlsw[0],hr_dlsw[1])).sum('sigma_wmf')\n", "llswwmf = ds3_hr_lpann_dt.WMF.isel(wmf_region=10).sel(sigma_wmf=slice(hr_lsw[0],hr_dlsw[0])).sum('sigma_wmf')\n", "xcorr4 = concat_correlations(moc45,lswwmf, lagrange)\n", "xcorr5 = concat_correlations(moc45,llswwmf, lagrange)\n", "xcorr6 = concat_correlations(moc45,dlswwmf, lagrange)\n", "ax6.set_xlabel('Lag (years)')\n", "ax6.set_ylim(ylim)\n", "ax6.set_xlim(xlim)\n", "tmpstr = r'$\\Psi_{max} \\; at \\; 45^{\\circ}N$'\n", "ax6.set_title(r'F) LSW (IRM+SPG-east) vs. '+tmpstr, fontdict={'size':fsize}, loc='left')\n", "plt6a = ax6.plot(xcorr4.lag, xcorr4.sel(stat='correlation'),color='k',linewidth=1)\n", "work6b = xcorr4.where(xcorr4.sel(stat='pval') < siglvl)\n", "lab6b = r'r($\\Psi_{max}$, LSW)'\n", "plt6b = ax6.plot(work6b.lag,work6b.sel(stat='correlation'), color='k', linewidth=3,label=lab6b)\n", "plt6c = ax6.plot(xcorr5.lag, xcorr5.sel(stat='correlation'),color='b',linewidth=1)\n", "work6d = xcorr5.where(xcorr5.sel(stat='pval') < siglvl)\n", "lab6c = r'r($\\Psi_{max}$, lLSW)'\n", "plt6d = ax6.plot(work6d.lag,work6d.sel(stat='correlation'), color='b', linewidth=3,label=lab6c)\n", "plt6e = ax6.plot(xcorr6.lag, xcorr6.sel(stat='correlation'),color='r',linewidth=1)\n", "work6f = xcorr6.where(xcorr6.sel(stat='pval') < siglvl)\n", "lab6d = r'r($\\Psi_{max}$, dLSW)'\n", "plt6f = ax6.plot(work6f.lag,work6f.sel(stat='correlation'), color='r', linewidth=3,label=lab6d)\n", "ax6.grid()\n", "ax6.set_yticks(yticks)\n", "ax6.set_xticks(xticks)\n", "ax6.legend(loc='lower right')\n", "\n", "#plt.savefig('fig_S4.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "CMIP6 2019.10", "language": "python", "name": "cmip6-201910" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 4 }