{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "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_RCP70_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": [ "# IMAU_VUB = IMAU_VUB\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_VUB_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_VUB_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_VUB_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_VUB_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_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": [ "0.5258 0.5263157894736842\n", "0.89265 0.8947368421052632\n", "0.52465 0.5263157894736842\n", "0.47015 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_IMAU_VUB_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R5_RCP70 = [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_IMAU_VUB_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R1_RCP70=np.vstack([SL_wTd_nos_base_IMAU_VUB_R1_RCP70, 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_IMAU_VUB_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R2_RCP70=np.vstack([SL_wTd_nos_base_IMAU_VUB_R2_RCP70, 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_IMAU_VUB_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R3_RCP70=np.vstack([SL_wTd_nos_base_IMAU_VUB_R3_RCP70, 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_IMAU_VUB_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R4_RCP70=np.vstack([SL_wTd_nos_base_IMAU_VUB_R4_RCP70, 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_IMAU_VUB_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R5_RCP70=np.vstack([SL_wTd_nos_base_IMAU_VUB_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_IMAU_VUB_SU_RCP70 = SL_wTd_nos_base_IMAU_VUB_R1_RCP70+SL_wTd_nos_base_IMAU_VUB_R2_RCP70+SL_wTd_nos_base_IMAU_VUB_R3_RCP70+SL_wTd_nos_base_IMAU_VUB_R4_RCP70+SL_wTd_nos_base_IMAU_VUB_R5_RCP70\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": 10, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_IMAU_VUB_RCP70.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_IMAU_VUB_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_IMAU_VUB_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_IMAU_VUB_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_IMAU_VUB_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_IMAU_VUB_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_IMAU_VUB_SU_RCP70\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": 11, "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_IMAU_VUB_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_IMAU_VUB_SU_RCP70_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 }