{ "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": [ "# 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": [ "0.52215 0.5263157894736842\n", "0.8927 0.8947368421052632\n", "0.52525 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_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R2_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R3_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_R4_RCP26 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R1_RCP26=np.vstack([SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R2_RCP26=np.vstack([SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R3_RCP26=np.vstack([SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R4_RCP26=np.vstack([SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_PISM_AWI_R5_RCP26=np.vstack([SL_wTd_nos_base_PISM_AWI_R5_RCP26, SL])\n", "\n", "SL_wTd_nos_base_PISM_AWI_SU_RCP26 = SL_wTd_nos_base_PISM_AWI_R1_RCP26+SL_wTd_nos_base_PISM_AWI_R2_RCP26+SL_wTd_nos_base_PISM_AWI_R3_RCP26+SL_wTd_nos_base_PISM_AWI_R4_RCP26+SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_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_PISM_AWI_R1_RCP26\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_AWI_R2_RCP26\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_AWI_R3_RCP26\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_AWI_R4_RCP26\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_AWI_R5_RCP26\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_AWI_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_PISM_AWI_SU_RCP26[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_AWI_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 }