{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import netCDF4 as nc\n", "import matplotlib.pylab as plt\n", "import imp\n", "import csv\n", "import pandas as pd\n", "import random as rnd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "651\n" ] } ], "source": [ "# Read atmospheric forcing\n", "\n", "NumTensemble = 600\n", "Tlen = 651\n", "\n", "fname = \"../MAGICC/CMIP5_RCP2500/Temp_CMIP5_RCP70_HistConstraint.dat\" # File to read\n", "df = pd.read_csv(fname,sep='\\s+',index_col=0,header=None)\n", "df.columns.name = \"ensemble member\"\n", "df.index.name = \"Time\"\n", "SAT = np.array(df.values)\n", "\n", "print(len(SAT[:,1]))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Normalize and crop temperature series\n", "Temp = []\n", "Tavebeg = 0\n", "Taveend = 80\n", "tbeg = 51\n", "tend = 251\n", "for i in range(len(SAT[1,:])):\n", " SATave = np.mean(SAT[Tavebeg:Taveend,i])\n", " SAT[:,i] = SAT[:,i]-SATave\n", "SAT = SAT[tbeg:tend,:]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Read ocean scaling\n", "\n", "NumOmodel = 19\n", "\n", "fname = \"../ScalingCoefficients/OceanScaling/OS_R1.dat\" # File to read\n", "with open(fname) as f:\n", " OS_NoDelay_R1 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n", "with open(fname) as f:\n", " OS_WiDelay_R1 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n", "with open(fname) as f:\n", " OS_Delay_R1 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n", "\n", "fname = \"../ScalingCoefficients/OceanScaling/OS_R2.dat\" # File to read\n", "with open(fname) as f:\n", " OS_NoDelay_R2 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n", "with open(fname) as f:\n", " OS_WiDelay_R2 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n", "with open(fname) as f:\n", " OS_Delay_R2 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n", "\n", "fname = \"../ScalingCoefficients/OceanScaling/OS_R3.dat\" # File to read\n", "with open(fname) as f:\n", " OS_NoDelay_R3 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n", "with open(fname) as f:\n", " OS_WiDelay_R3 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n", "with open(fname) as f:\n", " OS_Delay_R3 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n", "\n", "fname = \"../ScalingCoefficients/OceanScaling/OS_R4.dat\" # File to read\n", "with open(fname) as f:\n", " OS_NoDelay_R4 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n", "with open(fname) as f:\n", " OS_WiDelay_R4 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n", "with open(fname) as f:\n", " OS_Delay_R4 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Read melting sensitivity\n", "fname = \"../ScalingCoefficients/MeltSensitivity/MeltSensitivity.dat\" # File to read\n", "with open(fname) as f:\n", " MeltSensitivity = np.array([float(row) for row in f])\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# GRIS_LSC = GRIS_LSC\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R5 = np.array([float(row) for row in f])\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.52625 0.5263157894736842\n", "0.89235 0.8947368421052632\n", "0.52835 0.5263157894736842\n", "0.4746 0.47368421052631576\n" ] } ], "source": [ "EnsembleSize = 20000\n", "scaled_forcing = False\n", "\n", "countR1 = 0\n", "countR2 = 0\n", "countR3 = 0\n", "countR4 = 0\n", "\n", "SL_wTd_nos_base_GRIS_LSC_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R5_RCP70 = [0] * (tend-tbeg)\n", "\n", "for i in range(EnsembleSize):\n", "\n", " # Select forcing randomly\n", "\n", " # select global warming path\n", " iTens = rnd.randint(0,NumTensemble-1)\n", " Temp = np.array(SAT[:,iTens])\n", "\n", " # select ocean model\n", " iOmod = rnd.randint(0,NumOmodel-1)\n", " OS_R1 = OS_WiDelay_R1[iOmod]\n", " OS_R2 = OS_WiDelay_R2[iOmod]\n", " OS_R3 = OS_WiDelay_R3[iOmod]\n", " OS_R4 = OS_WiDelay_R4[iOmod]\n", " OS_R5 = OS_WiDelay_R4[iOmod]\n", "\n", " tau_R1 = int(OS_Delay_R1[iOmod])\n", " tau_R2 = int(OS_Delay_R2[iOmod])\n", " tau_R3 = int(OS_Delay_R3[iOmod])\n", " tau_R4 = int(OS_Delay_R4[iOmod])\n", " tau_R5 = int(OS_Delay_R4[iOmod])\n", "\n", " if tau_R1>0:\n", " countR1 = countR1+1\n", " if tau_R2>0:\n", " countR2 = countR2+1\n", " if tau_R3>0:\n", " countR3 = countR3+1\n", " if tau_R4>0:\n", " countR4 = countR4+1\n", " \n", " Temp_R1 = np.append(np.zeros(tau_R1),Temp[:tend-tbeg-tau_R1])\n", " Temp_R2 = np.append(np.zeros(tau_R2),Temp[:tend-tbeg-tau_R2])\n", " Temp_R3 = np.append(np.zeros(tau_R3),Temp[:tend-tbeg-tau_R3])\n", " Temp_R4 = np.append(np.zeros(tau_R4),Temp[:tend-tbeg-tau_R4])\n", " Temp_R5 = np.append(np.zeros(tau_R5),Temp[:tend-tbeg-tau_R5])\n", " \n", " # select melting sensitivity\n", " MS_R1 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R2 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R3 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R4 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R5 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", "\n", " # Compose forcing time series\n", " M_R1 = MS_R1*OS_R1*Temp_R1\n", " M_R2 = MS_R2*OS_R2*Temp_R2\n", " M_R3 = MS_R3*OS_R3*Temp_R3\n", " M_R4 = MS_R4*OS_R4*Temp_R4\n", " M_R5 = MS_R5*OS_R5*Temp_R5\n", "\n", " M_R1[M_R1 < 0.0] = 0.0\n", " M_R2[M_R2 < 0.0] = 0.0\n", " M_R3[M_R3 < 0.0] = 0.0\n", " M_R4[M_R4 < 0.0] = 0.0\n", " M_R5[M_R5 < 0.0] = 0.0\n", " \n", " # Linear response\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R1[tp]*RF_GRIS_LSC_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R1_RCP70=np.vstack([SL_wTd_nos_base_GRIS_LSC_R1_RCP70, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R2[tp]*RF_GRIS_LSC_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R2_RCP70=np.vstack([SL_wTd_nos_base_GRIS_LSC_R2_RCP70, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R3[tp]*RF_GRIS_LSC_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R3_RCP70=np.vstack([SL_wTd_nos_base_GRIS_LSC_R3_RCP70, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R4[tp]*RF_GRIS_LSC_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R4_RCP70=np.vstack([SL_wTd_nos_base_GRIS_LSC_R4_RCP70, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R5[tp]*RF_GRIS_LSC_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R5_RCP70=np.vstack([SL_wTd_nos_base_GRIS_LSC_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_GRIS_LSC_SU_RCP70 = SL_wTd_nos_base_GRIS_LSC_R1_RCP70+SL_wTd_nos_base_GRIS_LSC_R2_RCP70+SL_wTd_nos_base_GRIS_LSC_R3_RCP70+SL_wTd_nos_base_GRIS_LSC_R4_RCP70+SL_wTd_nos_base_GRIS_LSC_R5_RCP70\n", "\n", "print(countR1/EnsembleSize,10/19)\n", "print(countR2/EnsembleSize,17/19)\n", "print(countR3/EnsembleSize,10/19)\n", "print(countR4/EnsembleSize,9/19)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_GRIS_LSC_RCP70.nc','w', format='NETCDF4')\n", "ncfile.createDimension('Time', None)\n", "ncfile.createDimension('Emember', None)\n", "\n", "SL_wTd_nos_base_R1 = ncfile.createVariable('EAIS', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R2 = ncfile.createVariable('Ross', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R3 = ncfile.createVariable('Amundsen', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R4 = ncfile.createVariable('Weddell', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R5 = ncfile.createVariable('Peninsula', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_SU = ncfile.createVariable('Antarctica', 'f4', ('Emember', 'Time'))\n", "t = ncfile.createVariable('Time', 'i4', 'Time')\n", "\n", "SL_wTd_nos_base_R1[:,:] = SL_wTd_nos_base_GRIS_LSC_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_GRIS_LSC_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_GRIS_LSC_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_GRIS_LSC_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_GRIS_LSC_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_GRIS_LSC_SU_RCP70\n", "t[:] = Time\n", "\n", "SL_wTd_nos_base_R1.units = 'meter'\n", "SL_wTd_nos_base_R2.units = 'meter'\n", "SL_wTd_nos_base_R3.units = 'meter'\n", "SL_wTd_nos_base_R4.units = 'meter'\n", "SL_wTd_nos_base_R5.units = 'meter'\n", "SL_wTd_nos_base_SU.units = 'meter'\n", "\n", "t.units = 'years'\n", "\n", "ncfile.close()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fp3=plt.figure()\n", "for i in range(EnsembleSize):\n", " plt.plot(Time,SL_wTd_nos_base_GRIS_LSC_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_GRIS_LSC_SU_RCP70_alllines.pdf\", bbox_inches='tight')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }