{ "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_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": [ "0.5303 0.5263157894736842\n", "0.8907 0.8947368421052632\n", "0.52255 0.5263157894736842\n", "0.4644 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_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_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_JPL_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R1_RCP26=np.vstack([SL_wTd_nos_base_ISSM_JPL_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_JPL_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R2_RCP26=np.vstack([SL_wTd_nos_base_ISSM_JPL_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_JPL_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R3_RCP26=np.vstack([SL_wTd_nos_base_ISSM_JPL_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_JPL_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R4_RCP26=np.vstack([SL_wTd_nos_base_ISSM_JPL_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_JPL_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R5_RCP26=np.vstack([SL_wTd_nos_base_ISSM_JPL_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_ISSM_JPL_SU_RCP26 = SL_wTd_nos_base_ISSM_JPL_R1_RCP26+SL_wTd_nos_base_ISSM_JPL_R2_RCP26+SL_wTd_nos_base_ISSM_JPL_R3_RCP26+SL_wTd_nos_base_ISSM_JPL_R4_RCP26+SL_wTd_nos_base_ISSM_JPL_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_JPL_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_JPL_R1_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_ISSM_JPL_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_ISSM_JPL_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_ISSM_JPL_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_ISSM_JPL_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_ISSM_JPL_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_ISSM_JPL_SU_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_ISSM_JPL_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 }