{ "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_RCP85_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": [ "0.52615 0.5263157894736842\n", "0.89425 0.8947368421052632\n", "0.52905 0.5263157894736842\n", "0.47515 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_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R2_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R3_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R4_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PS3D_PSU_R5_RCP85 = [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_RCP85=np.vstack([SL_wTd_nos_base_PS3D_PSU_R1_RCP85, 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_RCP85=np.vstack([SL_wTd_nos_base_PS3D_PSU_R2_RCP85, 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_RCP85=np.vstack([SL_wTd_nos_base_PS3D_PSU_R3_RCP85, 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_RCP85=np.vstack([SL_wTd_nos_base_PS3D_PSU_R4_RCP85, 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_RCP85=np.vstack([SL_wTd_nos_base_PS3D_PSU_R5_RCP85, SL])\n", "\n", "SL_wTd_nos_base_PS3D_PSU_SU_RCP85 = SL_wTd_nos_base_PS3D_PSU_R1_RCP85+SL_wTd_nos_base_PS3D_PSU_R2_RCP85+SL_wTd_nos_base_PS3D_PSU_R3_RCP85+SL_wTd_nos_base_PS3D_PSU_R4_RCP85+SL_wTd_nos_base_PS3D_PSU_R5_RCP85\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_RCP85.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_RCP85\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PS3D_PSU_R2_RCP85\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PS3D_PSU_R3_RCP85\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PS3D_PSU_R4_RCP85\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PS3D_PSU_R5_RCP85\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PS3D_PSU_SU_RCP85\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_PS3D_PSU_SU_RCP85[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PS3D_PSU_SU_RCP85_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 }