{ "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_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]))\n", "# SAT[time,ensemblemember]" ] }, { "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": [ "# AISM_VUB = AISM_VUB\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_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": [ "0.52305 0.5263157894736842\n", "0.89225 0.8947368421052632\n", "0.53125 0.5263157894736842\n", "0.47095 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_AISM_VUB_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_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_AISM_VUB_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R1_RCP70=np.vstack([SL_wTd_nos_base_AISM_VUB_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_AISM_VUB_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R2_RCP70=np.vstack([SL_wTd_nos_base_AISM_VUB_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_AISM_VUB_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R3_RCP70=np.vstack([SL_wTd_nos_base_AISM_VUB_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_AISM_VUB_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R4_RCP70=np.vstack([SL_wTd_nos_base_AISM_VUB_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_AISM_VUB_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R5_RCP70=np.vstack([SL_wTd_nos_base_AISM_VUB_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_AISM_VUB_SU_RCP70 = SL_wTd_nos_base_AISM_VUB_R1_RCP70+SL_wTd_nos_base_AISM_VUB_R2_RCP70+SL_wTd_nos_base_AISM_VUB_R3_RCP70+SL_wTd_nos_base_AISM_VUB_R4_RCP70+SL_wTd_nos_base_AISM_VUB_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": 9, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_AISM_VUB_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_AISM_VUB_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_AISM_VUB_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_AISM_VUB_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_AISM_VUB_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_AISM_VUB_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_AISM_VUB_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": 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_AISM_VUB_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_AISM_VUB_SU_RCP70_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 }