{ "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": [ "# UA_UNN = UA_UNN\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_UA_UNN_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_UA_UNN_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_UA_UNN_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_UA_UNN_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_UA_UNN_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_UA_UNN_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_UA_UNN_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_UA_UNN_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_UA_UNN_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_UA_UNN_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": [ "0.5256 0.5263157894736842\n", "0.8943 0.8947368421052632\n", "0.52535 0.5263157894736842\n", "0.47305 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_UA_UNN_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_UA_UNN_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_UA_UNN_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_UA_UNN_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_UA_UNN_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_UA_UNN_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_UA_UNN_R1_RCP45=np.vstack([SL_wTd_nos_base_UA_UNN_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_UA_UNN_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_UA_UNN_R2_RCP45=np.vstack([SL_wTd_nos_base_UA_UNN_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_UA_UNN_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_UA_UNN_R3_RCP45=np.vstack([SL_wTd_nos_base_UA_UNN_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_UA_UNN_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_UA_UNN_R4_RCP45=np.vstack([SL_wTd_nos_base_UA_UNN_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_UA_UNN_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_UA_UNN_R5_RCP45=np.vstack([SL_wTd_nos_base_UA_UNN_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_UA_UNN_SU_RCP45 = SL_wTd_nos_base_UA_UNN_R1_RCP45+SL_wTd_nos_base_UA_UNN_R2_RCP45+SL_wTd_nos_base_UA_UNN_R3_RCP45+SL_wTd_nos_base_UA_UNN_R4_RCP45+SL_wTd_nos_base_UA_UNN_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_UA_UNN_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_UA_UNN_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_UA_UNN_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_UA_UNN_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_UA_UNN_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_UA_UNN_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_UA_UNN_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_UA_UNN_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_UA_UNN_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 }