{ "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": [ "# BISI_LBL = BISI_LBL\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_BISI_LBL_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_BISI_LBL_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": [ "0.5261 0.5263157894736842\n", "0.89645 0.8947368421052632\n", "0.52725 0.5263157894736842\n", "0.4678 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_BISI_LBL_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R1_RCP70=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R2_RCP70=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R3_RCP70=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R4_RCP70=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_BISI_LBL_R5_RCP70=np.vstack([SL_wTd_nos_base_BISI_LBL_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_BISI_LBL_SU_RCP70 = SL_wTd_nos_base_BISI_LBL_R1_RCP70+SL_wTd_nos_base_BISI_LBL_R2_RCP70+SL_wTd_nos_base_BISI_LBL_R3_RCP70+SL_wTd_nos_base_BISI_LBL_R4_RCP70+SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_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_BISI_LBL_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_BISI_LBL_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_BISI_LBL_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_BISI_LBL_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_BISI_LBL_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_BISI_LBL_SU_RCP70_alllines.pdf\", bbox_inches='tight')\n" ] }, { "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 }