{ "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]))\n", "# SAT[time,ensemblemember]" ] }, { "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": [ "# AISM_VUB = AISM_VUB\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_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": [ "0.52405 0.5263157894736842\n", "0.8964 0.8947368421052632\n", "0.52355 0.5263157894736842\n", "0.46905 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_AISM_VUB_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_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_AISM_VUB_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R1_RCP45=np.vstack([SL_wTd_nos_base_AISM_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_AISM_VUB_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R2_RCP45=np.vstack([SL_wTd_nos_base_AISM_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_AISM_VUB_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R3_RCP45=np.vstack([SL_wTd_nos_base_AISM_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_AISM_VUB_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R4_RCP45=np.vstack([SL_wTd_nos_base_AISM_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_AISM_VUB_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R5_RCP45=np.vstack([SL_wTd_nos_base_AISM_VUB_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_AISM_VUB_SU_RCP45 = SL_wTd_nos_base_AISM_VUB_R1_RCP45+SL_wTd_nos_base_AISM_VUB_R2_RCP45+SL_wTd_nos_base_AISM_VUB_R3_RCP45+SL_wTd_nos_base_AISM_VUB_R4_RCP45+SL_wTd_nos_base_AISM_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_AISM_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_AISM_VUB_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_AISM_VUB_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_AISM_VUB_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_AISM_VUB_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_AISM_VUB_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_AISM_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_AISM_VUB_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_AISM_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 }