{ "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_RCP45_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_AWI = PISM_AWI\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_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": [ "0.52555 0.5263157894736842\n", "0.8906 0.8947368421052632\n", "0.52945 0.5263157894736842\n", "0.47365 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_AWI_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R5_RCP45 = [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_AWI_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R1_RCP45=np.vstack([SL_wTd_nos_base_PISM_AWI_R1_RCP45, 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_AWI_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R2_RCP45=np.vstack([SL_wTd_nos_base_PISM_AWI_R2_RCP45, 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_AWI_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R3_RCP45=np.vstack([SL_wTd_nos_base_PISM_AWI_R3_RCP45, 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_AWI_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R4_RCP45=np.vstack([SL_wTd_nos_base_PISM_AWI_R4_RCP45, 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_AWI_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R5_RCP45=np.vstack([SL_wTd_nos_base_PISM_AWI_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_PISM_AWI_SU_RCP45 = SL_wTd_nos_base_PISM_AWI_R1_RCP45+SL_wTd_nos_base_PISM_AWI_R2_RCP45+SL_wTd_nos_base_PISM_AWI_R3_RCP45+SL_wTd_nos_base_PISM_AWI_R4_RCP45+SL_wTd_nos_base_PISM_AWI_R5_RCP45\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_AWI_RCP45.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_AWI_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_AWI_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_AWI_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_AWI_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_AWI_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_AWI_SU_RCP45\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_AWI_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_AWI_SU_RCP45_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 }