{ "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": [ "# 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": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5299 0.5263157894736842\n", "0.89915 0.8947368421052632\n", "0.5286 0.5263157894736842\n", "0.4814 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_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_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_IMAU_VUB_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R1_RCP26=np.vstack([SL_wTd_nos_base_IMAU_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_IMAU_VUB_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R2_RCP26=np.vstack([SL_wTd_nos_base_IMAU_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_IMAU_VUB_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R3_RCP26=np.vstack([SL_wTd_nos_base_IMAU_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_IMAU_VUB_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R4_RCP26=np.vstack([SL_wTd_nos_base_IMAU_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_IMAU_VUB_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R5_RCP26=np.vstack([SL_wTd_nos_base_IMAU_VUB_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_IMAU_VUB_SU_RCP26 = SL_wTd_nos_base_IMAU_VUB_R1_RCP26+SL_wTd_nos_base_IMAU_VUB_R2_RCP26+SL_wTd_nos_base_IMAU_VUB_R3_RCP26+SL_wTd_nos_base_IMAU_VUB_R4_RCP26+SL_wTd_nos_base_IMAU_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_IMAU_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_IMAU_VUB_R1_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_IMAU_VUB_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_IMAU_VUB_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_IMAU_VUB_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_IMAU_VUB_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_IMAU_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_IMAU_VUB_SU_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_IMAU_VUB_SU_RCP26_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 }