{ "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": [ "# ISSM_UCI = ISSM_UCI\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_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": [ "0.53075 0.5263157894736842\n", "0.89875 0.8947368421052632\n", "0.52715 0.5263157894736842\n", "0.4717 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_ISSM_UCI_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R1_RCP45=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R2_RCP45=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R3_RCP45=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R4_RCP45=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R5_RCP45=np.vstack([SL_wTd_nos_base_ISSM_UCI_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_ISSM_UCI_SU_RCP45 = SL_wTd_nos_base_ISSM_UCI_R1_RCP45+SL_wTd_nos_base_ISSM_UCI_R2_RCP45+SL_wTd_nos_base_ISSM_UCI_R3_RCP45+SL_wTd_nos_base_ISSM_UCI_R4_RCP45+SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_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_ISSM_UCI_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_ISSM_UCI_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_ISSM_UCI_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_ISSM_UCI_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_ISSM_UCI_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_ISSM_UCI_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 }