{ "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": [ "# MALI_LAN = MALI_LAN\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_MALI_LAN_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_MALI_LAN_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": [ "0.51965 0.5263157894736842\n", "0.89265 0.8947368421052632\n", "0.5229 0.5263157894736842\n", "0.47345 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_MALI_LAN_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R1_RCP70=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R2_RCP70=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R3_RCP70=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R4_RCP70=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R5_RCP70=np.vstack([SL_wTd_nos_base_MALI_LAN_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_MALI_LAN_SU_RCP70 = SL_wTd_nos_base_MALI_LAN_R1_RCP70+SL_wTd_nos_base_MALI_LAN_R2_RCP70+SL_wTd_nos_base_MALI_LAN_R3_RCP70+SL_wTd_nos_base_MALI_LAN_R4_RCP70+SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_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_MALI_LAN_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_MALI_LAN_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_MALI_LAN_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_MALI_LAN_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_MALI_LAN_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_MALI_LAN_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 }