{ "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": [ "# PS3D_PSU = PS3D_PSU\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_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": [ "0.5261 0.5263157894736842\n", "0.89745 0.8947368421052632\n", "0.5263 0.5263157894736842\n", "0.47815 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_PS3D_PSU_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R1_RCP45=np.vstack([SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R2_RCP45=np.vstack([SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R3_RCP45=np.vstack([SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R4_RCP45=np.vstack([SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R5_RCP45=np.vstack([SL_wTd_nos_base_PS3D_PSU_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_PS3D_PSU_SU_RCP45 = SL_wTd_nos_base_PS3D_PSU_R1_RCP45+SL_wTd_nos_base_PS3D_PSU_R2_RCP45+SL_wTd_nos_base_PS3D_PSU_R3_RCP45+SL_wTd_nos_base_PS3D_PSU_R4_RCP45+SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_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_PS3D_PSU_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PS3D_PSU_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PS3D_PSU_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PS3D_PSU_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PS3D_PSU_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PS3D_PSU_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 }