{ "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": [ "# 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": [ "0.5285 0.5263157894736842\n", "0.8966 0.8947368421052632\n", "0.5255 0.5263157894736842\n", "0.47575 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_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R1_RCP26=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R2_RCP26=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R3_RCP26=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R4_RCP26=np.vstack([SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_MALI_LAN_R5_RCP26=np.vstack([SL_wTd_nos_base_MALI_LAN_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_MALI_LAN_SU_RCP26 = SL_wTd_nos_base_MALI_LAN_R1_RCP26+SL_wTd_nos_base_MALI_LAN_R2_RCP26+SL_wTd_nos_base_MALI_LAN_R3_RCP26+SL_wTd_nos_base_MALI_LAN_R4_RCP26+SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_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_MALI_LAN_R1_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_MALI_LAN_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_MALI_LAN_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_MALI_LAN_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_MALI_LAN_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_MALI_LAN_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_MALI_LAN_SU_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_MALI_LAN_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 }