{ "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]))" ] }, { "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": [ "# PISM_VUW = PISM_VUW\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_VUW_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_VUW_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": [ "0.5239 0.5263157894736842\n", "0.89805 0.8947368421052632\n", "0.52345 0.5263157894736842\n", "0.4751 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_PISM_VUW_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_VUW_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_VUW_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_VUW_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_VUW_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_PISM_VUW_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R1_RCP70=np.vstack([SL_wTd_nos_base_PISM_VUW_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_PISM_VUW_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R2_RCP70=np.vstack([SL_wTd_nos_base_PISM_VUW_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_PISM_VUW_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R3_RCP70=np.vstack([SL_wTd_nos_base_PISM_VUW_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_PISM_VUW_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R4_RCP70=np.vstack([SL_wTd_nos_base_PISM_VUW_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_PISM_VUW_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_VUW_R5_RCP70=np.vstack([SL_wTd_nos_base_PISM_VUW_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_PISM_VUW_SU_RCP70 = SL_wTd_nos_base_PISM_VUW_R1_RCP70+SL_wTd_nos_base_PISM_VUW_R2_RCP70+SL_wTd_nos_base_PISM_VUW_R3_RCP70+SL_wTd_nos_base_PISM_VUW_R4_RCP70+SL_wTd_nos_base_PISM_VUW_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_PISM_VUW_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_PISM_VUW_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_VUW_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_VUW_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_VUW_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_VUW_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_VUW_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_PISM_VUW_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_VUW_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 }