{ "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_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": 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": [ "# ISSM_UCI = ISSM_UCI\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_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": [ "0.5293 0.5263157894736842\n", "0.89625 0.8947368421052632\n", "0.5262 0.5263157894736842\n", "0.4746 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_ISSM_UCI_R1_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R1_RCP26=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R2_RCP26=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R3_RCP26=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R4_RCP26=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R5_RCP26=np.vstack([SL_wTd_nos_base_ISSM_UCI_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_ISSM_UCI_SU_RCP26 = SL_wTd_nos_base_ISSM_UCI_R1_RCP26+SL_wTd_nos_base_ISSM_UCI_R2_RCP26+SL_wTd_nos_base_ISSM_UCI_R3_RCP26+SL_wTd_nos_base_ISSM_UCI_R4_RCP26+SL_wTd_nos_base_ISSM_UCI_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": 9, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_ISSM_UCI_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_ISSM_UCI_R1_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_ISSM_UCI_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_ISSM_UCI_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_ISSM_UCI_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_ISSM_UCI_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_ISSM_UCI_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": 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_ISSM_UCI_SU_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_ISSM_UCI_SU_RCP26_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 }