{ "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": [ "# GRIS_LSC = GRIS_LSC\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_GRIS_LSC_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_GRIS_LSC_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": [ "0.5226 0.5263157894736842\n", "0.8941 0.8947368421052632\n", "0.5282 0.5263157894736842\n", "0.47545 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_GRIS_LSC_R1_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R1_RCP26=np.vstack([SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R2_RCP26=np.vstack([SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R3_RCP26=np.vstack([SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R4_RCP26=np.vstack([SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_GRIS_LSC_R5_RCP26=np.vstack([SL_wTd_nos_base_GRIS_LSC_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_GRIS_LSC_SU_RCP26 = SL_wTd_nos_base_GRIS_LSC_R1_RCP26+SL_wTd_nos_base_GRIS_LSC_R2_RCP26+SL_wTd_nos_base_GRIS_LSC_R3_RCP26+SL_wTd_nos_base_GRIS_LSC_R4_RCP26+SL_wTd_nos_base_GRIS_LSC_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_GRIS_LSC_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_GRIS_LSC_R1_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_GRIS_LSC_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_GRIS_LSC_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_GRIS_LSC_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_GRIS_LSC_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_GRIS_LSC_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", 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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_GRIS_LSC_SU_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_GRIS_LSC_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 }