{ "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": [ "# MALI_LAN = MALI_LAN\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_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": [ "0.52125 0.5263157894736842\n", "0.8925 0.8947368421052632\n", "0.5194 0.5263157894736842\n", "0.4708 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_MALI_LAN_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R1_RCP45=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R2_RCP45=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R3_RCP45=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R4_RCP45=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R5_RCP45=np.vstack([SL_wTd_nos_base_MALI_LAN_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_MALI_LAN_SU_RCP45 = SL_wTd_nos_base_MALI_LAN_R1_RCP45+SL_wTd_nos_base_MALI_LAN_R2_RCP45+SL_wTd_nos_base_MALI_LAN_R3_RCP45+SL_wTd_nos_base_MALI_LAN_R4_RCP45+SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_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_MALI_LAN_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_MALI_LAN_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_MALI_LAN_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_MALI_LAN_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_MALI_LAN_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_MALI_LAN_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 }