{ "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_RCP26_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": [ "0.5296 0.5263157894736842\n", "0.8951 0.8947368421052632\n", "0.5241 0.5263157894736842\n", "0.47395 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_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_PIK_R5_RCP26 = [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_RCP26=np.vstack([SL_wTd_nos_base_PISM_PIK_R1_RCP26, 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_RCP26=np.vstack([SL_wTd_nos_base_PISM_PIK_R2_RCP26, 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_RCP26=np.vstack([SL_wTd_nos_base_PISM_PIK_R3_RCP26, 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_RCP26=np.vstack([SL_wTd_nos_base_PISM_PIK_R4_RCP26, 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_RCP26=np.vstack([SL_wTd_nos_base_PISM_PIK_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_PISM_PIK_SU_RCP26 = SL_wTd_nos_base_PISM_PIK_R1_RCP26+SL_wTd_nos_base_PISM_PIK_R2_RCP26+SL_wTd_nos_base_PISM_PIK_R3_RCP26+SL_wTd_nos_base_PISM_PIK_R4_RCP26+SL_wTd_nos_base_PISM_PIK_R5_RCP26\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_RCP26.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_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_PIK_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_PIK_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_PIK_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_PIK_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_PIK_SU_RCP26\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_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_PIK_SU_RCP26_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 }