{ "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_RCP45_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": [ "# PISM_DMI = PISM_DMI\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_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": [ "0.52815 0.5263157894736842\n", "0.89375 0.8947368421052632\n", "0.5271 0.5263157894736842\n", "0.47225 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_PISM_DMI_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_DMI_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_DMI_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_DMI_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_DMI_R5_RCP45 = [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_PISM_DMI_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R1_RCP45=np.vstack([SL_wTd_nos_base_PISM_DMI_R1_RCP45, 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_PISM_DMI_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R2_RCP45=np.vstack([SL_wTd_nos_base_PISM_DMI_R2_RCP45, 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_PISM_DMI_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R3_RCP45=np.vstack([SL_wTd_nos_base_PISM_DMI_R3_RCP45, 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_PISM_DMI_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R4_RCP45=np.vstack([SL_wTd_nos_base_PISM_DMI_R4_RCP45, 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_PISM_DMI_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R5_RCP45=np.vstack([SL_wTd_nos_base_PISM_DMI_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_PISM_DMI_SU_RCP45 = SL_wTd_nos_base_PISM_DMI_R1_RCP45+SL_wTd_nos_base_PISM_DMI_R2_RCP45+SL_wTd_nos_base_PISM_DMI_R3_RCP45+SL_wTd_nos_base_PISM_DMI_R4_RCP45+SL_wTd_nos_base_PISM_DMI_R5_RCP45\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_PISM_DMI_RCP45.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_PISM_DMI_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_DMI_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_DMI_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_DMI_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_DMI_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_DMI_SU_RCP45\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_PISM_DMI_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_DMI_SU_RCP45_alllines.pdf\", bbox_inches='tight')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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 }