{ "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": [ "# SICO_UHO = SICO_UHO\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_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": [ "0.5227 0.5263157894736842\n", "0.8964 0.8947368421052632\n", "0.52415 0.5263157894736842\n", "0.4754 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_SICO_UHO_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_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_SICO_UHO_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R1_RCP70=np.vstack([SL_wTd_nos_base_SICO_UHO_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_SICO_UHO_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R2_RCP70=np.vstack([SL_wTd_nos_base_SICO_UHO_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_SICO_UHO_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R3_RCP70=np.vstack([SL_wTd_nos_base_SICO_UHO_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_SICO_UHO_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R4_RCP70=np.vstack([SL_wTd_nos_base_SICO_UHO_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_SICO_UHO_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R5_RCP70=np.vstack([SL_wTd_nos_base_SICO_UHO_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_SICO_UHO_SU_RCP70 = SL_wTd_nos_base_SICO_UHO_R1_RCP70+SL_wTd_nos_base_SICO_UHO_R2_RCP70+SL_wTd_nos_base_SICO_UHO_R3_RCP70+SL_wTd_nos_base_SICO_UHO_R4_RCP70+SL_wTd_nos_base_SICO_UHO_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_SICO_UHO_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_SICO_UHO_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_SICO_UHO_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_SICO_UHO_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_SICO_UHO_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_SICO_UHO_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_SICO_UHO_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_SICO_UHO_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_SICO_UHO_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 }