{ "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": [ "# PISM_AWI = PISM_AWI\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_PISM_AWI_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_PISM_AWI_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": [ "0.5307 0.5263157894736842\n", "0.89565 0.8947368421052632\n", "0.5323 0.5263157894736842\n", "0.4753 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_PISM_AWI_R1_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R2_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R3_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R4_RCP70 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R1_RCP70=np.vstack([SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R2_RCP70=np.vstack([SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R3_RCP70=np.vstack([SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R4_RCP70=np.vstack([SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R5_RCP70=np.vstack([SL_wTd_nos_base_PISM_AWI_R5_RCP70, SL])\n", "\n", "SL_wTd_nos_base_PISM_AWI_SU_RCP70 = SL_wTd_nos_base_PISM_AWI_R1_RCP70+SL_wTd_nos_base_PISM_AWI_R2_RCP70+SL_wTd_nos_base_PISM_AWI_R3_RCP70+SL_wTd_nos_base_PISM_AWI_R4_RCP70+SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_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_PISM_AWI_R1_RCP70\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_AWI_R2_RCP70\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_AWI_R3_RCP70\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_AWI_R4_RCP70\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_AWI_R5_RCP70\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_SU_RCP70[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_AWI_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 }