{ "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": [ "# PS3D_PSU = PS3D_PSU\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PS3D_PSU_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PS3D_PSU_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": [ "0.52695 0.5263157894736842\n", "0.89535 0.8947368421052632\n", "0.53075 0.5263157894736842\n", "0.47295 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_PS3D_PSU_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R1_RCP70=np.vstack([SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R2_RCP70=np.vstack([SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R3_RCP70=np.vstack([SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R4_RCP70=np.vstack([SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PS3D_PSU_R5_RCP70=np.vstack([SL_wTd_nos_base_PS3D_PSU_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_PS3D_PSU_SU_RCP70 = SL_wTd_nos_base_PS3D_PSU_R1_RCP70+SL_wTd_nos_base_PS3D_PSU_R2_RCP70+SL_wTd_nos_base_PS3D_PSU_R3_RCP70+SL_wTd_nos_base_PS3D_PSU_R4_RCP70+SL_wTd_nos_base_PS3D_PSU_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_PS3D_PSU_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_PS3D_PSU_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PS3D_PSU_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PS3D_PSU_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PS3D_PSU_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PS3D_PSU_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PS3D_PSU_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", "text/plain": [ "
" ] }, "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_PS3D_PSU_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PS3D_PSU_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 }