{ "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_RCP45_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": [ "# CISM_NCA = CISM_NCA\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_CISM_NCA_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_CISM_NCA_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_CISM_NCA_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_CISM_NCA_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_CISM_NCA_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_CISM_NCA_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_CISM_NCA_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_CISM_NCA_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_CISM_NCA_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_CISM_NCA_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": [ "0.519 0.5263157894736842\n", "0.8964 0.8947368421052632\n", "0.52635 0.5263157894736842\n", "0.47205 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_CISM_NCA_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_CISM_NCA_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_CISM_NCA_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_CISM_NCA_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_CISM_NCA_R5_RCP45 = [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_CISM_NCA_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R1_RCP45=np.vstack([SL_wTd_nos_base_CISM_NCA_R1_RCP45, 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_CISM_NCA_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R2_RCP45=np.vstack([SL_wTd_nos_base_CISM_NCA_R2_RCP45, 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_CISM_NCA_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R3_RCP45=np.vstack([SL_wTd_nos_base_CISM_NCA_R3_RCP45, 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_CISM_NCA_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R4_RCP45=np.vstack([SL_wTd_nos_base_CISM_NCA_R4_RCP45, 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_CISM_NCA_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R5_RCP45=np.vstack([SL_wTd_nos_base_CISM_NCA_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_CISM_NCA_SU_RCP45 = SL_wTd_nos_base_CISM_NCA_R1_RCP45+SL_wTd_nos_base_CISM_NCA_R2_RCP45+SL_wTd_nos_base_CISM_NCA_R3_RCP45+SL_wTd_nos_base_CISM_NCA_R4_RCP45+SL_wTd_nos_base_CISM_NCA_R5_RCP45\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_CISM_NCA_RCP45.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_CISM_NCA_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_CISM_NCA_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_CISM_NCA_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_CISM_NCA_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_CISM_NCA_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_CISM_NCA_SU_RCP45\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_CISM_NCA_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_CISM_NCA_SU_RCP45_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 }