{ "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_RCP45_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": [ "# SICO_UHO = SICO_UHO\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_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": [ "0.5252 0.5263157894736842\n", "0.89035 0.8947368421052632\n", "0.53065 0.5263157894736842\n", "0.476 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_SICO_UHO_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R5_RCP45 = [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_SICO_UHO_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R1_RCP45=np.vstack([SL_wTd_nos_base_SICO_UHO_R1_RCP45, 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_SICO_UHO_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R2_RCP45=np.vstack([SL_wTd_nos_base_SICO_UHO_R2_RCP45, 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_SICO_UHO_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R3_RCP45=np.vstack([SL_wTd_nos_base_SICO_UHO_R3_RCP45, 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_SICO_UHO_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R4_RCP45=np.vstack([SL_wTd_nos_base_SICO_UHO_R4_RCP45, 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_SICO_UHO_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R5_RCP45=np.vstack([SL_wTd_nos_base_SICO_UHO_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_SICO_UHO_SU_RCP45 = SL_wTd_nos_base_SICO_UHO_R1_RCP45+SL_wTd_nos_base_SICO_UHO_R2_RCP45+SL_wTd_nos_base_SICO_UHO_R3_RCP45+SL_wTd_nos_base_SICO_UHO_R4_RCP45+SL_wTd_nos_base_SICO_UHO_R5_RCP45\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_SICO_UHO_RCP45.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_SICO_UHO_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_SICO_UHO_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_SICO_UHO_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_SICO_UHO_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_SICO_UHO_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_SICO_UHO_SU_RCP45\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_SICO_UHO_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_SICO_UHO_SU_RCP45_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 }