{ "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_VUW = PISM_VUW\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_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": [ "0.53505 0.5263157894736842\n", "0.89275 0.8947368421052632\n", "0.52625 0.5263157894736842\n", "0.46915 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_VUW_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_VUW_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_VUW_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_VUW_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_VUW_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_VUW_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R1_RCP45=np.vstack([SL_wTd_nos_base_PISM_VUW_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_VUW_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R2_RCP45=np.vstack([SL_wTd_nos_base_PISM_VUW_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_VUW_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R3_RCP45=np.vstack([SL_wTd_nos_base_PISM_VUW_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_VUW_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R4_RCP45=np.vstack([SL_wTd_nos_base_PISM_VUW_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_VUW_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R5_RCP45=np.vstack([SL_wTd_nos_base_PISM_VUW_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_PISM_VUW_SU_RCP45 = SL_wTd_nos_base_PISM_VUW_R1_RCP45+SL_wTd_nos_base_PISM_VUW_R2_RCP45+SL_wTd_nos_base_PISM_VUW_R3_RCP45+SL_wTd_nos_base_PISM_VUW_R4_RCP45+SL_wTd_nos_base_PISM_VUW_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_VUW_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_VUW_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_VUW_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_VUW_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_VUW_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_VUW_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_VUW_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", "text/plain": [ "
" ] }, "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_VUW_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_VUW_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 }