{ "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": [ "# FETI_VUB = FETI_VUB\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_VUB_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_VUB_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_VUB_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_VUB_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_VUB_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": [ "0.5214 0.5263157894736842\n", "0.89045 0.8947368421052632\n", "0.5289 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_FETI_VUB_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_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_FETI_VUB_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R1_RCP45=np.vstack([SL_wTd_nos_base_FETI_VUB_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_FETI_VUB_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R2_RCP45=np.vstack([SL_wTd_nos_base_FETI_VUB_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_FETI_VUB_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R3_RCP45=np.vstack([SL_wTd_nos_base_FETI_VUB_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_FETI_VUB_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R4_RCP45=np.vstack([SL_wTd_nos_base_FETI_VUB_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_FETI_VUB_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R5_RCP45=np.vstack([SL_wTd_nos_base_FETI_VUB_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_FETI_VUB_SU_RCP45 = SL_wTd_nos_base_FETI_VUB_R1_RCP45+SL_wTd_nos_base_FETI_VUB_R2_RCP45+SL_wTd_nos_base_FETI_VUB_R3_RCP45+SL_wTd_nos_base_FETI_VUB_R4_RCP45+SL_wTd_nos_base_FETI_VUB_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_FETI_VUB_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_FETI_VUB_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_FETI_VUB_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_FETI_VUB_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_FETI_VUB_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_FETI_VUB_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_FETI_VUB_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_FETI_VUB_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_FETI_VUB_SU_RCP45_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 }