{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "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": 11, "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": 12, "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": 13, "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": 14, "metadata": {}, "outputs": [], "source": [ "# BISI_LBL = BISI_LBL\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R5 = np.array([float(row) for row in f])\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.52155 0.5263157894736842\n", "0.89765 0.8947368421052632\n", "0.5272 0.5263157894736842\n", "0.47245 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_BISI_LBL_R1_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R1_RCP26=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R2_RCP26=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R3_RCP26=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R4_RCP26=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R5_RCP26=np.vstack([SL_wTd_nos_base_BISI_LBL_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_BISI_LBL_SU_RCP26 = SL_wTd_nos_base_BISI_LBL_R1_RCP26+SL_wTd_nos_base_BISI_LBL_R2_RCP26+SL_wTd_nos_base_BISI_LBL_R3_RCP26+SL_wTd_nos_base_BISI_LBL_R4_RCP26+SL_wTd_nos_base_BISI_LBL_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": 16, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_R1_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_BISI_LBL_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_BISI_LBL_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_BISI_LBL_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_BISI_LBL_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_BISI_LBL_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": 17, "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_BISI_LBL_SU_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_BISI_LBL_SU_RCP26_alllines.pdf\", bbox_inches='tight')\n" ] }, { "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 }