{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 12, "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": 13, "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_RCP70_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": 14, "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": 15, "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": 16, "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": 17, "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": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.52555 0.5263157894736842\n", "0.8923 0.8947368421052632\n", "0.526 0.5263157894736842\n", "0.47615 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_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_UA_UNN_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_UA_UNN_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_UA_UNN_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_UA_UNN_R5_RCP70 = [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_RCP70=np.vstack([SL_wTd_nos_base_UA_UNN_R1_RCP70, 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_RCP70=np.vstack([SL_wTd_nos_base_UA_UNN_R2_RCP70, 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_RCP70=np.vstack([SL_wTd_nos_base_UA_UNN_R3_RCP70, 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_RCP70=np.vstack([SL_wTd_nos_base_UA_UNN_R4_RCP70, 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_RCP70=np.vstack([SL_wTd_nos_base_UA_UNN_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_UA_UNN_SU_RCP70 = SL_wTd_nos_base_UA_UNN_R1_RCP70+SL_wTd_nos_base_UA_UNN_R2_RCP70+SL_wTd_nos_base_UA_UNN_R3_RCP70+SL_wTd_nos_base_UA_UNN_R4_RCP70+SL_wTd_nos_base_UA_UNN_R5_RCP70\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": 19, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_UA_UNN_RCP70.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_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_UA_UNN_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_UA_UNN_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_UA_UNN_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_UA_UNN_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_UA_UNN_SU_RCP70\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": 20, "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_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_UA_UNN_SU_RCP70_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 }