{ "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_PIK = PISM_PIK\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_PIK_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_PIK_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": [ "0.52285 0.5263157894736842\n", "0.90035 0.8947368421052632\n", "0.52945 0.5263157894736842\n", "0.47945 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_PIK_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R1_RCP70=np.vstack([SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R2_RCP70=np.vstack([SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R3_RCP70=np.vstack([SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R4_RCP70=np.vstack([SL_wTd_nos_base_PISM_PIK_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_PIK_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_PIK_R5_RCP70=np.vstack([SL_wTd_nos_base_PISM_PIK_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_PISM_PIK_SU_RCP70 = SL_wTd_nos_base_PISM_PIK_R1_RCP70+SL_wTd_nos_base_PISM_PIK_R2_RCP70+SL_wTd_nos_base_PISM_PIK_R3_RCP70+SL_wTd_nos_base_PISM_PIK_R4_RCP70+SL_wTd_nos_base_PISM_PIK_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_PIK_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_PIK_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_PIK_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_PIK_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_PIK_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_PIK_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_PIK_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_PIK_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_PIK_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 }