{ "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_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": 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": [ "# FETI_VUB = FETI_VUB\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_VUB_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_VUB_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_VUB_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_VUB_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_FETI_VUB_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_FETI_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": [ "0.52455 0.5263157894736842\n", "0.89735 0.8947368421052632\n", "0.5299 0.5263157894736842\n", "0.47915 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_FETI_VUB_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R1_RCP70=np.vstack([SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R2_RCP70=np.vstack([SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R3_RCP70=np.vstack([SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R4_RCP70=np.vstack([SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R5_RCP70=np.vstack([SL_wTd_nos_base_FETI_VUB_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_FETI_VUB_SU_RCP70 = SL_wTd_nos_base_FETI_VUB_R1_RCP70+SL_wTd_nos_base_FETI_VUB_R2_RCP70+SL_wTd_nos_base_FETI_VUB_R3_RCP70+SL_wTd_nos_base_FETI_VUB_R4_RCP70+SL_wTd_nos_base_FETI_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": 9, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_FETI_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_FETI_VUB_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_FETI_VUB_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_FETI_VUB_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_FETI_VUB_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_FETI_VUB_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_FETI_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": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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_FETI_VUB_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_FETI_VUB_SU_RCP70_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 }