{ "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_UCI = ISSM_UCI\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_UCI_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_UCI_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": [ "0.51945 0.5263157894736842\n", "0.89355 0.8947368421052632\n", "0.5267 0.5263157894736842\n", "0.4715 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_UCI_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_UCI_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_UCI_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R1_RCP70=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_UCI_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R2_RCP70=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_UCI_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R3_RCP70=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_UCI_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R4_RCP70=np.vstack([SL_wTd_nos_base_ISSM_UCI_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_UCI_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_UCI_R5_RCP70=np.vstack([SL_wTd_nos_base_ISSM_UCI_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_ISSM_UCI_SU_RCP70 = SL_wTd_nos_base_ISSM_UCI_R1_RCP70+SL_wTd_nos_base_ISSM_UCI_R2_RCP70+SL_wTd_nos_base_ISSM_UCI_R3_RCP70+SL_wTd_nos_base_ISSM_UCI_R4_RCP70+SL_wTd_nos_base_ISSM_UCI_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_UCI_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_UCI_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_ISSM_UCI_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_ISSM_UCI_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_ISSM_UCI_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_ISSM_UCI_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_ISSM_UCI_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", "text/plain": [ "
" ] }, "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_UCI_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_ISSM_UCI_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 }