{ "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": [ "# BISI_LBL = BISI_LBL\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_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", "0.89425 0.8947368421052632\n", "0.52805 0.5263157894736842\n", "0.47315 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_BISI_LBL_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R1_RCP45=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R2_RCP45=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R3_RCP45=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R4_RCP45=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R5_RCP45=np.vstack([SL_wTd_nos_base_BISI_LBL_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_BISI_LBL_SU_RCP45 = SL_wTd_nos_base_BISI_LBL_R1_RCP45+SL_wTd_nos_base_BISI_LBL_R2_RCP45+SL_wTd_nos_base_BISI_LBL_R3_RCP45+SL_wTd_nos_base_BISI_LBL_R4_RCP45+SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_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_BISI_LBL_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_BISI_LBL_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_BISI_LBL_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_BISI_LBL_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_BISI_LBL_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_BISI_LBL_SU_RCP45_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 }