{ "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": 4, "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_RCP26_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": 5, "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": 6, "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": 7, "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": 8, "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": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5267 0.5263157894736842\n", "0.89515 0.8947368421052632\n", "0.5245 0.5263157894736842\n", "0.4699 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_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R5_RCP26 = [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_RCP26=np.vstack([SL_wTd_nos_base_AISM_VUB_R1_RCP26, 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_RCP26=np.vstack([SL_wTd_nos_base_AISM_VUB_R2_RCP26, 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_RCP26=np.vstack([SL_wTd_nos_base_AISM_VUB_R3_RCP26, 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_RCP26=np.vstack([SL_wTd_nos_base_AISM_VUB_R4_RCP26, 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_RCP26=np.vstack([SL_wTd_nos_base_AISM_VUB_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_AISM_VUB_SU_RCP26 = SL_wTd_nos_base_AISM_VUB_R1_RCP26+SL_wTd_nos_base_AISM_VUB_R2_RCP26+SL_wTd_nos_base_AISM_VUB_R3_RCP26+SL_wTd_nos_base_AISM_VUB_R4_RCP26+SL_wTd_nos_base_AISM_VUB_R5_RCP26\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": 17, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_AISM_VUB_RCP26.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_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_AISM_VUB_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_AISM_VUB_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_AISM_VUB_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_AISM_VUB_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_AISM_VUB_SU_RCP26\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": 18, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfp3\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mEnsembleSize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTime\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mSL_wTd_nos_base_AISM_VUB_SU_RCP26\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m 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\u001b[0mstickies\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "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_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_AISM_VUB_SU_RCP26_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 }