{ "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_PIK = PISM_PIK\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_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": [ "0.526 0.5263157894736842\n", "0.8929 0.8947368421052632\n", "0.5288 0.5263157894736842\n", "0.47505 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_PIK_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R1_RCP45=np.vstack([SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R2_RCP45=np.vstack([SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R3_RCP45=np.vstack([SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R4_RCP45=np.vstack([SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R5_RCP45=np.vstack([SL_wTd_nos_base_PISM_PIK_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_PISM_PIK_SU_RCP45 = SL_wTd_nos_base_PISM_PIK_R1_RCP45+SL_wTd_nos_base_PISM_PIK_R2_RCP45+SL_wTd_nos_base_PISM_PIK_R3_RCP45+SL_wTd_nos_base_PISM_PIK_R4_RCP45+SL_wTd_nos_base_PISM_PIK_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_PIK_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_PIK_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_PIK_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_PIK_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_PIK_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_PIK_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_PIK_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_PIK_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_PIK_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 }