{ "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_DMI = PISM_DMI\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_DMI_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_DMI_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": [ "0.52035 0.5263157894736842\n", "0.89285 0.8947368421052632\n", "0.52305 0.5263157894736842\n", "0.4714 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_DMI_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_DMI_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_DMI_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_DMI_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R1_RCP70=np.vstack([SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R2_RCP70=np.vstack([SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R3_RCP70=np.vstack([SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R4_RCP70=np.vstack([SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_DMI_R5_RCP70=np.vstack([SL_wTd_nos_base_PISM_DMI_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_PISM_DMI_SU_RCP70 = SL_wTd_nos_base_PISM_DMI_R1_RCP70+SL_wTd_nos_base_PISM_DMI_R2_RCP70+SL_wTd_nos_base_PISM_DMI_R3_RCP70+SL_wTd_nos_base_PISM_DMI_R4_RCP70+SL_wTd_nos_base_PISM_DMI_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_DMI_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_DMI_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_DMI_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_DMI_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_DMI_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_DMI_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_DMI_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_DMI_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_DMI_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 }