{ "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": [ "# GRIS_LSC = GRIS_LSC\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_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": [ "0.5219 0.5263157894736842\n", "0.89275 0.8947368421052632\n", "0.5257 0.5263157894736842\n", "0.47005 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_GRIS_LSC_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R1_RCP45=np.vstack([SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R2_RCP45=np.vstack([SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R3_RCP45=np.vstack([SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R4_RCP45=np.vstack([SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R5_RCP45=np.vstack([SL_wTd_nos_base_GRIS_LSC_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_GRIS_LSC_SU_RCP45 = SL_wTd_nos_base_GRIS_LSC_R1_RCP45+SL_wTd_nos_base_GRIS_LSC_R2_RCP45+SL_wTd_nos_base_GRIS_LSC_R3_RCP45+SL_wTd_nos_base_GRIS_LSC_R4_RCP45+SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_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_GRIS_LSC_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_GRIS_LSC_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_GRIS_LSC_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_GRIS_LSC_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_GRIS_LSC_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_GRIS_LSC_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 }