{ "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_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": 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": [ "0.52195 0.5263157894736842\n", "0.8962 0.8947368421052632\n", "0.5257 0.5263157894736842\n", "0.47695 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_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_VUB_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R1_RCP26=np.vstack([SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R2_RCP26=np.vstack([SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R3_RCP26=np.vstack([SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R4_RCP26=np.vstack([SL_wTd_nos_base_FETI_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_FETI_VUB_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_FETI_VUB_R5_RCP26=np.vstack([SL_wTd_nos_base_FETI_VUB_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_FETI_VUB_SU_RCP26 = SL_wTd_nos_base_FETI_VUB_R1_RCP26+SL_wTd_nos_base_FETI_VUB_R2_RCP26+SL_wTd_nos_base_FETI_VUB_R3_RCP26+SL_wTd_nos_base_FETI_VUB_R4_RCP26+SL_wTd_nos_base_FETI_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": 9, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_FETI_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_FETI_VUB_R1_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_FETI_VUB_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_FETI_VUB_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_FETI_VUB_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_FETI_VUB_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_FETI_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": 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_FETI_VUB_SU_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_FETI_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 }