{ "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": [ "# ISSM_JPL = ISSM_JPL\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_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": [ "0.52165 0.5263157894736842\n", "0.8957 0.8947368421052632\n", "0.52545 0.5263157894736842\n", "0.47495 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_JPL_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_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_ISSM_JPL_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R1_RCP70=np.vstack([SL_wTd_nos_base_ISSM_JPL_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_ISSM_JPL_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R2_RCP70=np.vstack([SL_wTd_nos_base_ISSM_JPL_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_ISSM_JPL_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R3_RCP70=np.vstack([SL_wTd_nos_base_ISSM_JPL_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_ISSM_JPL_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R4_RCP70=np.vstack([SL_wTd_nos_base_ISSM_JPL_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_ISSM_JPL_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R5_RCP70=np.vstack([SL_wTd_nos_base_ISSM_JPL_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_ISSM_JPL_SU_RCP70 = SL_wTd_nos_base_ISSM_JPL_R1_RCP70+SL_wTd_nos_base_ISSM_JPL_R2_RCP70+SL_wTd_nos_base_ISSM_JPL_R3_RCP70+SL_wTd_nos_base_ISSM_JPL_R4_RCP70+SL_wTd_nos_base_ISSM_JPL_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_ISSM_JPL_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_ISSM_JPL_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_ISSM_JPL_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_ISSM_JPL_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_ISSM_JPL_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_ISSM_JPL_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_ISSM_JPL_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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" ] }, "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_JPL_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_ISSM_JPL_SU_RCP70_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 }