{ "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": [ "# 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": [ "0.528 0.5263157894736842\n", "0.89205 0.8947368421052632\n", "0.5299 0.5263157894736842\n", "0.47595 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_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_CISM_NCA_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_CISM_NCA_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_CISM_NCA_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R1_RCP70=np.vstack([SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R2_RCP70=np.vstack([SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R3_RCP70=np.vstack([SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R4_RCP70=np.vstack([SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_CISM_NCA_R5_RCP70=np.vstack([SL_wTd_nos_base_CISM_NCA_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_CISM_NCA_SU_RCP70 = SL_wTd_nos_base_CISM_NCA_R1_RCP70+SL_wTd_nos_base_CISM_NCA_R2_RCP70+SL_wTd_nos_base_CISM_NCA_R3_RCP70+SL_wTd_nos_base_CISM_NCA_R4_RCP70+SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_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_CISM_NCA_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_CISM_NCA_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_CISM_NCA_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_CISM_NCA_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_CISM_NCA_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_CISM_NCA_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", 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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_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_CISM_NCA_SU_RCP70_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 }